diff --git a/alr_envs/__init__.py b/alr_envs/__init__.py index f814080..0007982 100644 --- a/alr_envs/__init__.py +++ b/alr_envs/__init__.py @@ -1,7 +1,8 @@ from gym.envs.registration import register from alr_envs.stochastic_search.functions.f_rosenbrock import Rosenbrock -# from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper + +# from alr_envs.utils.mps.dmp_wrapper import DmpWrapper # Mujoco @@ -71,6 +72,17 @@ register( } ) +## Balancing Reacher + +register( + id='Balancing-v0', + entry_point='alr_envs.mujoco:BalancingEnv', + max_episode_steps=200, + kwargs={ + "n_links": 5, + } +) + register( id='ALRBallInACupSimple-v0', entry_point='alr_envs.mujoco:ALRBallInACupEnv', @@ -101,15 +113,7 @@ register( # Classic control -register( - id='Balancing-v0', - entry_point='alr_envs.mujoco:BalancingEnv', - max_episode_steps=200, - kwargs={ - "n_links": 5, - } -) - +## Simple Reacher register( id='SimpleReacher-v0', entry_point='alr_envs.classic_control:SimpleReacherEnv', @@ -119,6 +123,16 @@ register( } ) +register( + id='SimpleReacher-v1', + entry_point='alr_envs.classic_control:SimpleReacherEnv', + max_episode_steps=200, + kwargs={ + "n_links": 2, + "random_start": False + } +) + register( id='LongSimpleReacher-v0', entry_point='alr_envs.classic_control:SimpleReacherEnv', @@ -128,6 +142,18 @@ register( } ) +register( + id='LongSimpleReacher-v1', + entry_point='alr_envs.classic_control:SimpleReacherEnv', + max_episode_steps=200, + kwargs={ + "n_links": 5, + "random_start": False + } +) + +## Viapoint Reacher + register( id='ViaPointReacher-v0', entry_point='alr_envs.classic_control.viapoint_reacher:ViaPointReacher', @@ -139,14 +165,47 @@ register( } ) +## Hole Reacher register( id='HoleReacher-v0', - entry_point='alr_envs.classic_control.hole_reacher:HoleReacher', + entry_point='alr_envs.classic_control.hole_reacher:HoleReacherEnv', max_episode_steps=200, kwargs={ "n_links": 5, "allow_self_collision": False, "allow_wall_collision": False, + "hole_width": None, + "hole_depth": 1, + "hole_x": None, + "collision_penalty": 100, + } +) + +register( + id='HoleReacher-v1', + entry_point='alr_envs.classic_control.hole_reacher:HoleReacherEnv', + max_episode_steps=200, + kwargs={ + "n_links": 5, + "random_start": False, + "allow_self_collision": False, + "allow_wall_collision": False, + "hole_width": None, + "hole_depth": 1, + "hole_x": None, + "collision_penalty": 100, + } +) + +register( + id='HoleReacher-v2', + entry_point='alr_envs.classic_control.hole_reacher:HoleReacherEnv', + max_episode_steps=200, + kwargs={ + "n_links": 5, + "random_start": False, + "allow_self_collision": False, + "allow_wall_collision": False, "hole_width": 0.25, "hole_depth": 1, "hole_x": 2, @@ -155,6 +214,25 @@ register( ) # MP environments +## Simple Reacher +versions = ["SimpleReacher-v0", "SimpleReacher-v1", "LongSimpleReacher-v0", "LongSimpleReacher-v1"] +for v in versions: + name = v.split("-") + register( + id=f'{name[0]}DMP-{name[1]}', + entry_point='alr_envs.utils.make_env_helpers:make_dmp_env', + # max_episode_steps=1, + kwargs={ + "name": f"alr_envs:{v}", + "num_dof": 2 if "long" not in v.lower() else 5, + "num_basis": 5, + "duration": 2, + "alpha_phase": 2, + "learn_goal": True, + "policy_type": "velocity", + "weights_scale": 50, + } + ) register( id='ViaPointReacherDMP-v0', @@ -172,23 +250,33 @@ register( } ) -register( - id='HoleReacherDMP-v0', - entry_point='alr_envs.utils.make_env_helpers:make_dmp_env', - # max_episode_steps=1, - kwargs={ - "name": "alr_envs:HoleReacher-v0", - "num_dof": 5, - "num_basis": 5, - "duration": 2, - "learn_goal": True, - "alpha_phase": 2, - "bandwidth_factor": 2, - "policy_type": "velocity", - "weights_scale": 50, - "goal_scale": 0.1 - } -) +## Hole Reacher +versions = ["v0", "v1", "v2"] +for v in versions: + register( + id=f'HoleReacherDMP-{v}', + entry_point='alr_envs.utils.make_env_helpers:make_dmp_env', + # max_episode_steps=1, + kwargs={ + "name": f"alr_envs:HoleReacher-{v}", + "num_dof": 5, + "num_basis": 5, + "duration": 2, + "learn_goal": True, + "alpha_phase": 2, + "bandwidth_factor": 2, + "policy_type": "velocity", + "weights_scale": 50, + "goal_scale": 0.1 + } + ) + +# register( +# id='HoleReacherDetPMP-v0', +# entry_point='alr_envs.classic_control.hole_reacher:holereacher_detpmp', +# # max_episode_steps=1, +# # TODO: add mp kwargs +# ) # TODO: properly add final_pos register( @@ -208,12 +296,7 @@ register( } ) -# register( -# id='HoleReacherDetPMP-v0', -# entry_point='alr_envs.classic_control.hole_reacher:holereacher_detpmp', -# # max_episode_steps=1, -# # TODO: add mp kwargs -# ) +## Ball in Cup register( id='ALRBallInACupSimpleDMP-v0', diff --git a/alr_envs/classic_control/__init__.py b/alr_envs/classic_control/__init__.py index 8d31d19..4a26eaa 100644 --- a/alr_envs/classic_control/__init__.py +++ b/alr_envs/classic_control/__init__.py @@ -1,3 +1,3 @@ from alr_envs.classic_control.simple_reacher import SimpleReacherEnv from alr_envs.classic_control.viapoint_reacher import ViaPointReacher -from alr_envs.classic_control.hole_reacher import HoleReacher +from alr_envs.classic_control.hole_reacher import HoleReacherEnv diff --git a/alr_envs/classic_control/hole_reacher.py b/alr_envs/classic_control/hole_reacher.py index 3b382f9..0e008aa 100644 --- a/alr_envs/classic_control/hole_reacher.py +++ b/alr_envs/classic_control/hole_reacher.py @@ -1,27 +1,36 @@ +from typing import Union + import gym -import numpy as np import matplotlib.pyplot as plt +import numpy as np +from gym.utils import seeding from matplotlib import patches + from alr_envs.classic_control.utils import check_self_collision +from alr_envs.utils.mps.mp_environments import MPEnv -class HoleReacher(gym.Env): +class HoleReacherEnv(MPEnv): - def __init__(self, n_links, hole_x, hole_width, hole_depth, allow_self_collision=False, - allow_wall_collision=False, collision_penalty=1000): + def __init__(self, n_links: int, hole_x: Union[None, float] = None, hole_depth: Union[None, float] = None, + hole_width: float = 1., random_start: bool = False, allow_self_collision: bool = False, + allow_wall_collision: bool = False, collision_penalty: bool = 1000): self.n_links = n_links self.link_lengths = np.ones((n_links, 1)) - # task - self.hole_x = hole_x # x-position of center of hole - self.hole_width = hole_width # width of hole - self.hole_depth = hole_depth # depth of hole + self.random_start = random_start - self.bottom_center_of_hole = np.hstack([hole_x, -hole_depth]) - self.top_center_of_hole = np.hstack([hole_x, 0]) - self.left_wall_edge = np.hstack([hole_x - self.hole_width / 2, 0]) - self.right_wall_edge = np.hstack([hole_x + self.hole_width / 2, 0]) + # provided initial parameters + self._hole_x = hole_x # x-position of center of hole + self._hole_width = hole_width # width of hole + self._hole_depth = hole_depth # depth of hole + + # temp container for current env state + self._tmp_hole_x = None + self._tmp_hole_width = None + self._tmp_hole_depth = None + self._goal = None # x-y coordinates for reaching the center at the bottom of the hole # collision self.allow_self_collision = allow_self_collision @@ -32,91 +41,77 @@ class HoleReacher(gym.Env): self._joints = None self._joint_angles = None self._angle_velocity = None - self.start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)]) - self.start_vel = np.zeros(self.n_links) + self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)]) + self._start_vel = np.zeros(self.n_links) self.dt = 0.01 - # self.time_limit = 2 action_bound = np.pi * np.ones((self.n_links,)) state_bound = np.hstack([ [np.pi] * self.n_links, # cos [np.pi] * self.n_links, # sin [np.inf] * self.n_links, # velocity + [np.inf], # hole width + [np.inf], # hole depth [np.inf] * 2, # x-y coordinates of target distance [np.inf] # env steps, because reward start after n steps TODO: Maybe ]) self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape) self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape) + # containers for plotting + self.metadata = {'render.modes': ["human", "partial"]} self.fig = None - rect_1 = patches.Rectangle((-self.n_links, -1), - self.n_links + self.hole_x - self.hole_width / 2, 1, - fill=True, edgecolor='k', facecolor='k') - rect_2 = patches.Rectangle((self.hole_x + self.hole_width / 2, -1), - self.n_links - self.hole_x + self.hole_width / 2, 1, - fill=True, edgecolor='k', facecolor='k') - rect_3 = patches.Rectangle((self.hole_x - self.hole_width / 2, -1), self.hole_width, - 1 - self.hole_depth, - fill=True, edgecolor='k', facecolor='k') - self.patches = [rect_1, rect_2, rect_3] + self._steps = 0 + self.seed() - @property - def end_effector(self): - return self._joints[self.n_links].T + def step(self, action: np.ndarray): + """ + A single step with an action in joint velocity space + """ - def configure(self, context): - pass + self._angle_velocity = action + self._joint_angles = self._joint_angles + self.dt * self._angle_velocity + self._update_joints() + + acc = (action - self._angle_velocity) / self.dt + reward, info = self._get_reward(acc) + + info.update({"is_collided": self._is_collided}) + + self._steps += 1 + done = self._is_collided + + return self._get_obs().copy(), reward, done, info def reset(self): - self._joint_angles = self.start_pos - self._angle_velocity = self.start_vel + if self.random_start: + # Maybe change more than dirst seed + first_joint = self.np_random.uniform(np.pi / 4, 3 * np.pi / 4) + self._joint_angles = np.hstack([[first_joint], np.zeros(self.n_links - 1)]) + self._start_pos = self._joint_angles.copy() + else: + self._joint_angles = self._start_pos + + self._generate_hole() + self._set_patches() + + self._angle_velocity = self._start_vel self._joints = np.zeros((self.n_links + 1, 2)) self._update_joints() self._steps = 0 return self._get_obs().copy() - def step(self, action: np.ndarray): - """ - a single step with an action in joint velocity space - """ - vel = action # + 0.01 * np.random.randn(self.num_links) - acc = (vel - self._angle_velocity) / self.dt - self._angle_velocity = vel - self._joint_angles = self._joint_angles + self.dt * self._angle_velocity - - self._update_joints() - - # rew = self._reward() - - # compute reward directly in step function - - success = False - reward = 0 - if not self._is_collided: - if self._steps == 199: - dist = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) - reward = - dist ** 2 - success = dist < 0.005 - else: - dist = np.linalg.norm(self.end_effector - self.bottom_center_of_hole) - # if self.collision_penalty != 0: - # reward = -self.collision_penalty - # else: - reward = - dist ** 2 - self.collision_penalty - - reward -= 5e-8 * np.sum(acc ** 2) - - info = {"is_collided": self._is_collided, "is_success": success} - - self._steps += 1 - - # done = self._steps * self.dt > self.time_limit or self._is_collided - done = self._is_collided - - return self._get_obs().copy(), reward, done, info + def _generate_hole(self): + self._tmp_hole_x = self.np_random.uniform(0.5, 3.5, 1) if self._hole_x is None else np.copy(self._hole_x) + self._tmp_hole_width = self.np_random.uniform(0.5, 0.1, 1) if self._hole_width is None else np.copy( + self._hole_width) + # TODO we do not want this right now. + self._tmp_hole_depth = self.np_random.uniform(1, 1, 1) if self._hole_depth is None else np.copy( + self._hole_depth) + self._goal = np.hstack([self._tmp_hole_x, -self._tmp_hole_depth]) def _update_joints(self): """ @@ -124,7 +119,7 @@ class HoleReacher(gym.Env): Returns: """ - line_points_in_taskspace = self.get_forward_kinematics(num_points_per_link=20) + line_points_in_taskspace = self._get_forward_kinematics(num_points_per_link=20) self._joints[1:, 0] = self._joints[0, 0] + line_points_in_taskspace[:, -1, 0] self._joints[1:, 1] = self._joints[0, 1] + line_points_in_taskspace[:, -1, 1] @@ -138,48 +133,65 @@ class HoleReacher(gym.Env): self_collision = True if not self.allow_wall_collision: - wall_collision = self.check_wall_collision(line_points_in_taskspace) + wall_collision = self._check_wall_collision(line_points_in_taskspace) self._is_collided = self_collision or wall_collision + def _get_reward(self, acc: np.ndarray): + success = False + reward = -np.inf + if not self._is_collided: + dist = 0 + # return reward only in last time step + if self._steps == 199: + dist = np.linalg.norm(self.end_effector - self._goal) + success = dist < 0.005 + else: + # Episode terminates when colliding, hence return reward + dist = np.linalg.norm(self.end_effector - self._goal) + reward = -self.collision_penalty + + reward -= dist ** 2 + reward -= 5e-8 * np.sum(acc ** 2) + info = {"is_success": success} + + return reward, info + def _get_obs(self): theta = self._joint_angles return np.hstack([ np.cos(theta), np.sin(theta), self._angle_velocity, - self.end_effector - self.bottom_center_of_hole, + self._tmp_hole_width, + self._tmp_hole_depth, + self.end_effector - self._goal, self._steps ]) - def get_forward_kinematics(self, num_points_per_link=1): + def _get_forward_kinematics(self, num_points_per_link=1): theta = self._joint_angles[:, None] - if num_points_per_link > 1: - intermediate_points = np.linspace(0, 1, num_points_per_link) - else: - intermediate_points = 1 - + intermediate_points = np.linspace(0, 1, num_points_per_link) if num_points_per_link > 1 else 1 accumulated_theta = np.cumsum(theta, axis=0) - - endeffector = np.zeros(shape=(self.n_links, num_points_per_link, 2)) + end_effector = np.zeros(shape=(self.n_links, num_points_per_link, 2)) x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points - endeffector[0, :, 0] = x[0, :] - endeffector[0, :, 1] = y[0, :] + end_effector[0, :, 0] = x[0, :] + end_effector[0, :, 1] = y[0, :] for i in range(1, self.n_links): - endeffector[i, :, 0] = x[i, :] + endeffector[i - 1, -1, 0] - endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1] + end_effector[i, :, 0] = x[i, :] + end_effector[i - 1, -1, 0] + end_effector[i, :, 1] = y[i, :] + end_effector[i - 1, -1, 1] - return np.squeeze(endeffector + self._joints[0, :]) + return np.squeeze(end_effector + self._joints[0, :]) - def check_wall_collision(self, line_points): + def _check_wall_collision(self, line_points): # all points that are before the hole in x - r, c = np.where(line_points[:, :, 0] < (self.hole_x - self.hole_width / 2)) + r, c = np.where(line_points[:, :, 0] < (self._tmp_hole_x - self._tmp_hole_width / 2)) # check if any of those points are below surface nr_line_points_below_surface_before_hole = np.sum(line_points[r, c, 1] < 0) @@ -188,7 +200,7 @@ class HoleReacher(gym.Env): return True # all points that are after the hole in x - r, c = np.where(line_points[:, :, 0] > (self.hole_x + self.hole_width / 2)) + r, c = np.where(line_points[:, :, 0] > (self._tmp_hole_x + self._tmp_hole_width / 2)) # check if any of those points are below surface nr_line_points_below_surface_after_hole = np.sum(line_points[r, c, 1] < 0) @@ -197,11 +209,11 @@ class HoleReacher(gym.Env): return True # all points that are above the hole - r, c = np.where((line_points[:, :, 0] > (self.hole_x - self.hole_width / 2)) & ( - line_points[:, :, 0] < (self.hole_x + self.hole_width / 2))) + r, c = np.where((line_points[:, :, 0] > (self._tmp_hole_x - self._tmp_hole_width / 2)) & ( + line_points[:, :, 0] < (self._tmp_hole_x + self._tmp_hole_width / 2))) # check if any of those points are below surface - nr_line_points_below_surface_in_hole = np.sum(line_points[r, c, 1] < -self.hole_depth) + nr_line_points_below_surface_in_hole = np.sum(line_points[r, c, 1] < -self._tmp_hole_depth) if nr_line_points_below_surface_in_hole > 0: return True @@ -210,61 +222,85 @@ class HoleReacher(gym.Env): def render(self, mode='human'): if self.fig is None: + # Create base figure once on the beginning. Afterwards only update + plt.ion() self.fig = plt.figure() - # plt.ion() - # plt.pause(0.01) - else: - plt.figure(self.fig.number) + ax = self.fig.add_subplot(1, 1, 1) + + # limits + lim = np.sum(self.link_lengths) + 0.5 + ax.set_xlim([-lim, lim]) + ax.set_ylim([-1.1, lim]) + + self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + self._set_patches() + self.fig.show() + + self.fig.gca().set_title( + f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}") if mode == "human": - plt.cla() - plt.title(f"Iteration: {self._steps}, distance: {self.end_effector - self.bottom_center_of_hole}") - # Arm - plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + # arm + self.line.set_data(self._joints[:, 0], self._joints[:, 1]) - # Add the patch to the Axes - [plt.gca().add_patch(rect) for rect in self.patches] - - lim = np.sum(self.link_lengths) + 0.5 - plt.xlim([-lim, lim]) - plt.ylim([-1.1, lim]) - # plt.draw() - plt.pause(1e-4) # pushes window to foreground, which is annoying. - # self.fig.canvas.flush_events() + self.fig.canvas.draw() + self.fig.canvas.flush_events() elif mode == "partial": - if self._steps == 1: - # fig, ax = plt.subplots() - # Add the patch to the Axes - [plt.gca().add_patch(rect) for rect in self.patches] - # plt.pause(0.01) - if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided: # Arm - plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k', alpha=self._steps / 200) - # ax.plot(line_points_in_taskspace[:, 0, 0], - # line_points_in_taskspace[:, 0, 1], - # line_points_in_taskspace[:, -1, 0], - # line_points_in_taskspace[:, -1, 1], marker='o', color='k', alpha=t / 200) + plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k', + alpha=self._steps / 200) - lim = np.sum(self.link_lengths) + 0.5 - plt.xlim([-lim, lim]) - plt.ylim([-1.1, lim]) - plt.pause(0.01) + def _set_patches(self): + if self.fig is not None: + self.fig.gca().patches = [] + left_block = patches.Rectangle((-self.n_links, -self._tmp_hole_depth), + self.n_links + self._tmp_hole_x - self._tmp_hole_width / 2, + self._tmp_hole_depth, + fill=True, edgecolor='k', facecolor='k') + right_block = patches.Rectangle((self._tmp_hole_x + self._tmp_hole_width / 2, -self._tmp_hole_depth), + self.n_links - self._tmp_hole_x + self._tmp_hole_width / 2, + self._tmp_hole_depth, + fill=True, edgecolor='k', facecolor='k') + hole_floor = patches.Rectangle((self._tmp_hole_x - self._tmp_hole_width / 2, -self._tmp_hole_depth), + self._tmp_hole_width, + 1 - self._tmp_hole_depth, + fill=True, edgecolor='k', facecolor='k') - elif mode == "final": - if self._steps == 199 or self._is_collided: - # fig, ax = plt.subplots() + # Add the patch to the Axes + self.fig.gca().add_patch(left_block) + self.fig.gca().add_patch(right_block) + self.fig.gca().add_patch(hole_floor) - # Add the patch to the Axes - [plt.gca().add_patch(rect) for rect in self.patches] + @property + def active_obs(self): + return np.hstack([ + [self.random_start] * self.n_links, # cos + [self.random_start] * self.n_links, # sin + [self.random_start] * self.n_links, # velocity + [self._hole_width is None], # hole width + [self._hole_depth is None], # hole width + [True] * 2, # x-y coordinates of target distance + [False] # env steps + ]) - plt.xlim(-self.n_links, self.n_links), plt.ylim(-1, self.n_links) - # Arm - plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + @property + def start_pos(self) -> Union[float, int, np.ndarray]: + return self._start_pos - plt.pause(0.01) + @property + def goal_pos(self) -> Union[float, int, np.ndarray]: + raise ValueError("Goal position is not available and has to be learnt based on the environment.") + + def seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + @property + def end_effector(self): + return self._joints[self.n_links].T def close(self): if self.fig is not None: @@ -274,22 +310,20 @@ class HoleReacher(gym.Env): if __name__ == '__main__': nl = 5 render_mode = "human" # "human" or "partial" or "final" - env = HoleReacher(n_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=0.15, - hole_depth=1, hole_x=1) - env.reset() - # env.render(mode=render_mode) + env = HoleReacherEnv(n_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=None, + hole_depth=1, hole_x=None) + obs = env.reset() for i in range(200): # objective.load_result("/tmp/cma") # test with random actions ac = 2 * env.action_space.sample() - # ac[0] += np.pi/2 obs, rew, d, info = env.step(ac) env.render(mode=render_mode) print(rew) if d: - break + env.reset() env.close() diff --git a/alr_envs/classic_control/simple_reacher.py b/alr_envs/classic_control/simple_reacher.py index 296662c..425134d 100644 --- a/alr_envs/classic_control/simple_reacher.py +++ b/alr_envs/classic_control/simple_reacher.py @@ -1,39 +1,41 @@ -import gym +from typing import Iterable, Union + import matplotlib.pyplot as plt import numpy as np from gym import spaces from gym.utils import seeding -from alr_envs.utils.utils import angle_normalize +from alr_envs.utils.mps.mp_environments import MPEnv -# if os.environ.get("DISPLAY", None): -# mpl.use('Qt5Agg') - - -class SimpleReacherEnv(gym.Env): +class SimpleReacherEnv(MPEnv): """ Simple Reaching Task without any physics simulation. Returns no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions towards the end of the trajectory. """ - def __init__(self, n_links): + def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True): super().__init__() self.link_lengths = np.ones(n_links) self.n_links = n_links self.dt = 0.1 - self._goal_pos = None + self.random_start = random_start self._joints = None - self._joint_angle = None + self._joint_angles = None self._angle_velocity = None + self._start_pos = np.zeros(self.n_links) + self._start_vel = np.zeros(self.n_links) - self.max_torque = 1 # 10 + self._target = target # provided target value + self._goal = None # updated goal value, does not change when target != None + + self.max_torque = 1 self.steps_before_reward = 199 - action_bound = np.ones((self.n_links,)) + action_bound = np.ones((self.n_links,)) * self.max_torque state_bound = np.hstack([ [np.pi] * self.n_links, # cos [np.pi] * self.n_links, # sin @@ -44,49 +46,50 @@ class SimpleReacherEnv(gym.Env): self.action_space = spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape) self.observation_space = spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape) - self.fig = None + # containers for plotting self.metadata = {'render.modes': ["human"]} + self.fig = None self._steps = 0 self.seed() def step(self, action: np.ndarray): + """ + A single step with action in torque space + """ # action = self._add_action_noise(action) - action = np.clip(action, -self.max_torque, self.max_torque) + ac = np.clip(action, -self.max_torque, self.max_torque) - self._angle_velocity = self._angle_velocity + self.dt * action - self._joint_angle = angle_normalize(self._joint_angle + self.dt * self._angle_velocity) + self._angle_velocity = self._angle_velocity + self.dt * ac + self._joint_angles = self._joint_angles + self.dt * self._angle_velocity self._update_joints() - self._steps += 1 reward, info = self._get_reward(action) - # done = np.abs(self.end_effector - self._goal_pos) < 0.1 + self._steps += 1 done = False return self._get_obs().copy(), reward, done, info - def _add_action_noise(self, action: np.ndarray): - """ - add unobserved Gaussian Noise N(0,0.01) to the actions - Args: - action: + def reset(self): - Returns: actions with noise + # TODO: maybe do initialisation more random? + # Sample only orientation of first link, i.e. the arm is always straight. + if self.random_start: + self._joint_angles = np.hstack([[self.np_random.uniform(-np.pi, np.pi)], np.zeros(self.n_links - 1)]) + self._start_pos = self._joint_angles.copy() + else: + self._joint_angles = self._start_pos - """ - return self.np_random.normal(0, 0.1, *action.shape) + action + self._generate_goal() - def _get_obs(self): - theta = self._joint_angle - return np.hstack([ - np.cos(theta), - np.sin(theta), - self._angle_velocity, - self.end_effector - self._goal_pos, - self._steps - ]) + self._angle_velocity = self._start_vel + self._joints = np.zeros((self.n_links + 1, 2)) + self._update_joints() + self._steps = 0 + + return self._get_obs().copy() def _update_joints(self): """ @@ -94,15 +97,14 @@ class SimpleReacherEnv(gym.Env): Returns: """ - angles = np.cumsum(self._joint_angle) + angles = np.cumsum(self._joint_angles) x = self.link_lengths * np.vstack([np.cos(angles), np.sin(angles)]) self._joints[1:] = self._joints[0] + np.cumsum(x.T, axis=0) def _get_reward(self, action: np.ndarray): - diff = self.end_effector - self._goal_pos + diff = self.end_effector - self._goal reward_dist = 0 - # TODO: Is this the best option if self._steps >= self.steps_before_reward: reward_dist -= np.linalg.norm(diff) # reward_dist = np.exp(-0.1 * diff ** 2).mean() @@ -112,62 +114,112 @@ class SimpleReacherEnv(gym.Env): reward = reward_dist - reward_ctrl return reward, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl) - def reset(self): + def _get_obs(self): + theta = self._joint_angles + return np.hstack([ + np.cos(theta), + np.sin(theta), + self._angle_velocity, + self.end_effector - self._goal, + self._steps + ]) - # TODO: maybe do initialisation more random? - # Sample only orientation of first link, i.e. the arm is always straight. - self._joint_angle = np.hstack([[self.np_random.uniform(-np.pi, np.pi)], np.zeros(self.n_links - 1)]) - self._angle_velocity = np.zeros(self.n_links) - self._joints = np.zeros((self.n_links + 1, 2)) - self._update_joints() - self._steps = 0 + def _generate_goal(self): - self._goal_pos = self._get_random_goal() - return self._get_obs().copy() + if self._target is None: - def _get_random_goal(self): - center = self._joints[0] + total_length = np.sum(self.link_lengths) + goal = np.array([total_length, total_length]) + while np.linalg.norm(goal) >= total_length: + goal = self.np_random.uniform(low=-total_length, high=total_length, size=2) + else: + goal = np.copy(self._target) - # Sample uniformly in circle with radius R around center of reacher. - R = np.sum(self.link_lengths) - r = R * np.sqrt(self.np_random.uniform()) - theta = self.np_random.uniform() * 2 * np.pi - return center + r * np.stack([np.cos(theta), np.sin(theta)]) + self._goal = goal + + def render(self, mode='human'): # pragma: no cover + if self.fig is None: + # Create base figure once on the beginning. Afterwards only update + plt.ion() + self.fig = plt.figure() + ax = self.fig.add_subplot(1, 1, 1) + + # limits + lim = np.sum(self.link_lengths) + 0.5 + ax.set_xlim([-lim, lim]) + ax.set_ylim([-lim, lim]) + + self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + goal_pos = self._goal.T + self.goal_point, = ax.plot(goal_pos[0], goal_pos[1], 'gx') + self.goal_dist, = ax.plot([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]], 'g--') + + self.fig.show() + + self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}") + + # goal + goal_pos = self._goal.T + if self._steps == 1: + self.goal_point.set_data(goal_pos[0], goal_pos[1]) + + # arm + self.line.set_data(self._joints[:, 0], self._joints[:, 1]) + + # distance between end effector and goal + self.goal_dist.set_data([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]]) + + self.fig.canvas.draw() + self.fig.canvas.flush_events() + + @property + def active_obs(self): + return np.hstack([ + [self.random_start] * self.n_links, # cos + [self.random_start] * self.n_links, # sin + [self.random_start] * self.n_links, # velocity + [True] * 2, # x-y coordinates of target distance + [False] # env steps + ]) + + @property + def start_pos(self): + return self._start_pos + + @property + def goal_pos(self): + raise ValueError("Goal position is not available and has to be learnt based on the environment.") def seed(self, seed=None): self.np_random, seed = seeding.np_random(seed) return [seed] - def render(self, mode='human'): # pragma: no cover - if self.fig is None: - self.fig = plt.figure() - plt.ion() - plt.show() - else: - plt.figure(self.fig.number) - - plt.cla() - plt.title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal_pos}") - - # Arm - plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') - - # goal - goal_pos = self._goal_pos.T - plt.plot(goal_pos[0], goal_pos[1], 'gx') - # distance between end effector and goal - plt.plot([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]], 'g--') - - lim = np.sum(self.link_lengths) + 0.5 - plt.xlim([-lim, lim]) - plt.ylim([-lim, lim]) - # plt.draw() - # plt.pause(1e-4) pushes window to foreground, which is annoying. - self.fig.canvas.flush_events() - def close(self): del self.fig @property def end_effector(self): return self._joints[self.n_links].T + + +if __name__ == '__main__': + nl = 5 + render_mode = "human" # "human" or "partial" or "final" + env = SimpleReacherEnv(n_links=nl) + obs = env.reset() + print("First", obs) + + for i in range(2000): + # objective.load_result("/tmp/cma") + # test with random actions + ac = 2 * env.action_space.sample() + # ac = np.ones(env.action_space.shape) + obs, rew, d, info = env.step(ac) + env.render(mode=render_mode) + + print(obs[env.active_obs].shape) + + if d or i % 200 == 0: + env.reset() + + env.close() diff --git a/alr_envs/classic_control/viapoint_reacher.py b/alr_envs/classic_control/viapoint_reacher.py index 127bf77..2897f31 100644 --- a/alr_envs/classic_control/viapoint_reacher.py +++ b/alr_envs/classic_control/viapoint_reacher.py @@ -1,19 +1,31 @@ +from typing import Iterable, Union + import gym import matplotlib.pyplot as plt import numpy as np +from gym.utils import seeding from alr_envs.classic_control.utils import check_self_collision +from alr_envs.utils.mps.mp_environments import MPEnv -class ViaPointReacher(gym.Env): +class ViaPointReacher(MPEnv): - def __init__(self, n_links, allow_self_collision=False, collision_penalty=1000): - self.num_links = n_links + def __init__(self, n_links, random_start: bool = True, via_target: Union[None, Iterable] = None, + target: Union[None, Iterable] = None, allow_self_collision=False, collision_penalty=1000): + + self.n_links = n_links self.link_lengths = np.ones((n_links, 1)) - # task - self.via_point = np.ones(2) - self.goal_point = np.array((n_links, 0)) + self.random_start = random_start + + # provided initial parameters + self._target = target # provided target value + self._via_target = via_target # provided via point target value + + # temp container for current env state + self._via_point = np.ones(2) + self._goal = np.array((n_links, 0)) # collision self.allow_self_collision = allow_self_collision @@ -23,78 +35,93 @@ class ViaPointReacher(gym.Env): self._joints = None self._joint_angles = None self._angle_velocity = None - self.start_pos = np.hstack([[np.pi / 2], np.zeros(self.num_links - 1)]) - self.start_vel = np.zeros(self.num_links) + self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)]) + self._start_vel = np.zeros(self.n_links) self.weight_matrix_scale = 1 - self._steps = 0 self.dt = 0.01 - # self.time_limit = 2 - action_bound = np.pi * np.ones((self.num_links,)) + action_bound = np.pi * np.ones((self.n_links,)) state_bound = np.hstack([ - [np.pi] * self.num_links, # cos - [np.pi] * self.num_links, # sin - [np.inf] * self.num_links, # velocity + [np.pi] * self.n_links, # cos + [np.pi] * self.n_links, # sin + [np.inf] * self.n_links, # velocity + [np.inf] * 2, # x-y coordinates of via point distance [np.inf] * 2, # x-y coordinates of target distance - [np.inf] # env steps, because reward start after n steps TODO: Maybe + [np.inf] # env steps, because reward start after n steps ]) self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape) self.observation_space = gym.spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape) + # containers for plotting + self.metadata = {'render.modes': ["human", "partial"]} self.fig = None - @property - def end_effector(self): - return self._joints[self.num_links].T - - def configure(self, context): - pass - - def reset(self): - self._joint_angles = self.start_pos - self._angle_velocity = self.start_vel - self._joints = np.zeros((self.num_links + 1, 2)) - self._update_joints() self._steps = 0 - - return self._get_obs().copy() + self.seed() def step(self, action: np.ndarray): """ a single step with an action in joint velocity space """ vel = action - acc = (vel - self._angle_velocity) / self.dt self._angle_velocity = vel self._joint_angles = self._joint_angles + self.dt * self._angle_velocity - self._update_joints() - dist_reward = 0 - if not self._is_collided: - if self._steps == 100: - dist_reward = np.linalg.norm(self.end_effector - self.via_point) - elif self._steps == 199: - dist_reward = np.linalg.norm(self.end_effector - self.goal_point) + acc = (vel - self._angle_velocity) / self.dt + reward, info = self._get_reward(acc) - # TODO: Do we need that? - reward = - dist_reward ** 2 - - reward -= 5e-8 * np.sum(acc**2) - - if self._is_collided: - reward -= self.collision_penalty - - info = {"is_collided": self._is_collided} + info.update({"is_collided": self._is_collided}) self._steps += 1 - - # done = self._steps * self.dt > self.time_limit or self._is_collided done = self._is_collided return self._get_obs().copy(), reward, done, info + def reset(self): + + if self.random_start: + # Maybe change more than dirst seed + first_joint = self.np_random.uniform(np.pi / 4, 3 * np.pi / 4) + self._joint_angles = np.hstack([[first_joint], np.zeros(self.n_links - 1)]) + self._start_pos = self._joint_angles.copy() + else: + self._joint_angles = self._start_pos + + self._generate_goal() + + self._angle_velocity = self._start_vel + self._joints = np.zeros((self.n_links + 1, 2)) + self._update_joints() + self._steps = 0 + + return self._get_obs().copy() + + def _generate_goal(self): + # TODO: Maybe improve this later, this can yield quite a lot of invalid settings + + total_length = np.sum(self.link_lengths) + + # rejection sampled point in inner circle with 0.5*Radius + if self._via_target is None: + via_target = np.array([total_length, total_length]) + while np.linalg.norm(via_target) >= 0.5 * total_length: + via_target = self.np_random.uniform(low=-0.5 * total_length, high=0.5 * total_length, size=2) + else: + via_target = np.copy(self._via_target) + + # rejection sampled point in outer circle + if self._target is None: + goal = np.array([total_length, total_length]) + while np.linalg.norm(goal) >= total_length or np.linalg.norm(goal) <= 0.5 * total_length: + goal = self.np_random.uniform(low=-total_length, high=total_length, size=2) + else: + goal = np.copy(self._target) + + self._via_target = via_target + self._goal = goal + def _update_joints(self): """ update _joints to get new end effector position. The other links are only required for rendering. @@ -115,14 +142,38 @@ class ViaPointReacher(gym.Env): self._is_collided = self_collision + def _get_reward(self, acc): + success = False + reward = -np.inf + if not self._is_collided: + dist = np.inf + # return intermediate reward for via point + if self._steps == 100: + dist = np.linalg.norm(self.end_effector - self._via_point) + # return reward in last time step for goal + elif self._steps == 199: + dist = np.linalg.norm(self.end_effector - self._goal) + + success = dist < 0.005 + else: + # Episode terminates when colliding, hence return reward + dist = np.linalg.norm(self.end_effector - self._goal) + reward = -self.collision_penalty + + reward -= dist ** 2 + reward -= 5e-8 * np.sum(acc ** 2) + info = {"is_success": success} + + return reward, info + def _get_obs(self): theta = self._joint_angles return np.hstack([ np.cos(theta), np.sin(theta), self._angle_velocity, - self.end_effector - self.via_point, - self.end_effector - self.goal_point, + self.end_effector - self._via_point, + self.end_effector - self._goal, self._steps ]) @@ -133,7 +184,7 @@ class ViaPointReacher(gym.Env): accumulated_theta = np.cumsum(theta, axis=0) - endeffector = np.zeros(shape=(self.num_links, num_points_per_link, 2)) + endeffector = np.zeros(shape=(self.n_links, num_points_per_link, 2)) x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points @@ -141,33 +192,46 @@ class ViaPointReacher(gym.Env): endeffector[0, :, 0] = x[0, :] endeffector[0, :, 1] = y[0, :] - for i in range(1, self.num_links): + for i in range(1, self.n_links): endeffector[i, :, 0] = x[i, :] + endeffector[i - 1, -1, 0] endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1] return np.squeeze(endeffector + self._joints[0, :]) def render(self, mode='human'): + goal_pos = self._goal.T + via_pos = self._via_point.T + if self.fig is None: + # Create base figure once on the beginning. Afterwards only update + plt.ion() self.fig = plt.figure() - # plt.ion() - # plt.pause(0.01) - else: - plt.figure(self.fig.number) + ax = self.fig.add_subplot(1, 1, 1) + + # limits + lim = np.sum(self.link_lengths) + 0.5 + ax.set_xlim([-lim, lim]) + ax.set_ylim([-lim, lim]) + + self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + self.goal_point_plot, = ax.plot(goal_pos[0], goal_pos[1], 'go') + self.via_point_plot, = ax.plot(via_pos[0], via_pos[1], 'gx') + + self.fig.show() + + self.fig.gca().set_title(f"Iteration: {self._steps}, distance: {self.end_effector - self._goal}") if mode == "human": - plt.cla() - plt.title(f"Iteration: {self._steps}") + # goal + if self._steps == 1: + self.goal_point_plot.set_data(goal_pos[0], goal_pos[1]) + self.via_point_plot.set_data(via_pos[0], goal_pos[1]) - # Arm - plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') + # arm + self.line.set_data(self._joints[:, 0], self._joints[:, 1]) - lim = np.sum(self.link_lengths) + 0.5 - plt.xlim([-lim, lim]) - plt.ylim([-lim, lim]) - # plt.draw() - plt.pause(1e-4) # pushes window to foreground, which is annoying. - # self.fig.canvas.flush_events() + self.fig.canvas.draw() + self.fig.canvas.flush_events() elif mode == "partial": if self._steps == 1: @@ -196,12 +260,39 @@ class ViaPointReacher(gym.Env): # Add the patch to the Axes [plt.gca().add_patch(rect) for rect in self.patches] - plt.xlim(-self.num_links, self.num_links), plt.ylim(-1, self.num_links) + plt.xlim(-self.n_links, self.n_links), plt.ylim(-1, self.n_links) # Arm plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') plt.pause(0.01) + @property + def active_obs(self): + return np.hstack([ + [self.random_start] * self.n_links, # cos + [self.random_start] * self.n_links, # sin + [self.random_start] * self.n_links, # velocity + [self._via_target is None] * 2, # x-y coordinates of via point distance + [True] * 2, # x-y coordinates of target distance + [False] # env steps + ]) + + @property + def start_pos(self) -> Union[float, int, np.ndarray]: + return self._start_pos + + @property + def goal_pos(self) -> Union[float, int, np.ndarray]: + raise ValueError("Goal position is not available and has to be learnt based on the environment.") + + def seed(self, seed=None): + self.np_random, seed = seeding.np_random(seed) + return [seed] + + @property + def end_effector(self): + return self._joints[self.n_links].T + def close(self): if self.fig is not None: plt.close(self.fig) diff --git a/alr_envs/mujoco/ball_in_a_cup/utils.py b/alr_envs/mujoco/ball_in_a_cup/utils.py index bfec3cf..714566a 100644 --- a/alr_envs/mujoco/ball_in_a_cup/utils.py +++ b/alr_envs/mujoco/ball_in_a_cup/utils.py @@ -1,5 +1,5 @@ -from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper -from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper +from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper +from alr_envs.utils.mps.dmp_wrapper import DmpWrapper from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup import ALRBallInACupEnv @@ -17,19 +17,8 @@ def make_contextual_env(rank, seed=0): def _init(): env = ALRBallInACupEnv(reward_type="contextual_goal") - env = DetPMPWrapper(env, - num_dof=7, - num_basis=5, - width=0.005, - policy_type="motor", - start_pos=env.start_pos, - duration=3.5, - post_traj_time=4.5, - dt=env.dt, - weights_scale=0.5, - zero_start=True, - zero_goal=True - ) + env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5, + policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True) env.seed(seed + rank) return env @@ -51,19 +40,8 @@ def make_env(rank, seed=0): def _init(): env = ALRBallInACupEnv(reward_type="simple") - env = DetPMPWrapper(env, - num_dof=7, - num_basis=5, - width=0.005, - policy_type="motor", - start_pos=env.start_pos, - duration=3.5, - post_traj_time=4.5, - dt=env.dt, - weights_scale=0.2, - zero_start=True, - zero_goal=True - ) + env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5, + policy_type="motor", weights_scale=0.2, zero_start=True, zero_goal=True) env.seed(seed + rank) return env @@ -85,20 +63,8 @@ def make_simple_env(rank, seed=0): def _init(): env = ALRBallInACupEnv(reward_type="simple") - env = DetPMPWrapper(env, - num_dof=3, - num_basis=5, - width=0.005, - off=-0.1, - policy_type="motor", - start_pos=env.start_pos[1::2], - duration=3.5, - post_traj_time=4.5, - dt=env.dt, - weights_scale=0.25, - zero_start=True, - zero_goal=True - ) + env = DetPMPWrapper(env, num_dof=3, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5, + policy_type="motor", weights_scale=0.25, zero_start=True, zero_goal=True, off=-0.1) env.seed(seed + rank) return env diff --git a/alr_envs/mujoco/beerpong/utils.py b/alr_envs/mujoco/beerpong/utils.py index bfbc2a1..37d2ad1 100644 --- a/alr_envs/mujoco/beerpong/utils.py +++ b/alr_envs/mujoco/beerpong/utils.py @@ -1,4 +1,4 @@ -from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper +from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper from alr_envs.mujoco.beerpong.beerpong import ALRBeerpongEnv from alr_envs.mujoco.beerpong.beerpong_simple import ALRBeerpongEnv as ALRBeerpongEnvSimple @@ -17,19 +17,8 @@ def make_contextual_env(rank, seed=0): def _init(): env = ALRBeerpongEnv() - env = DetPMPWrapper(env, - num_dof=7, - num_basis=5, - width=0.005, - policy_type="motor", - start_pos=env.start_pos, - duration=3.5, - post_traj_time=4.5, - dt=env.dt, - weights_scale=0.5, - zero_start=True, - zero_goal=True - ) + env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5, + policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True) env.seed(seed + rank) return env @@ -51,19 +40,8 @@ def make_env(rank, seed=0): def _init(): env = ALRBeerpongEnvSimple() - env = DetPMPWrapper(env, - num_dof=7, - num_basis=5, - width=0.005, - policy_type="motor", - start_pos=env.start_pos, - duration=3.5, - post_traj_time=4.5, - dt=env.dt, - weights_scale=0.25, - zero_start=True, - zero_goal=True - ) + env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5, + policy_type="motor", weights_scale=0.25, zero_start=True, zero_goal=True) env.seed(seed + rank) return env @@ -85,19 +63,8 @@ def make_simple_env(rank, seed=0): def _init(): env = ALRBeerpongEnvSimple() - env = DetPMPWrapper(env, - num_dof=3, - num_basis=5, - width=0.005, - policy_type="motor", - start_pos=env.start_pos[1::2], - duration=3.5, - post_traj_time=4.5, - dt=env.dt, - weights_scale=0.5, - zero_start=True, - zero_goal=True - ) + env = DetPMPWrapper(env, num_dof=3, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5, + policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True) env.seed(seed + rank) return env diff --git a/alr_envs/mujoco/reacher/alr_reacher.py b/alr_envs/mujoco/reacher/alr_reacher.py index c6cca16..e27e069 100644 --- a/alr_envs/mujoco/reacher/alr_reacher.py +++ b/alr_envs/mujoco/reacher/alr_reacher.py @@ -2,12 +2,12 @@ import os import numpy as np from gym import utils -from gym.envs.mujoco import mujoco_env +from gym.envs.mujoco import MujocoEnv import alr_envs.utils.utils as alr_utils -class ALRReacherEnv(mujoco_env.MujocoEnv, utils.EzPickle): +class ALRReacherEnv(MujocoEnv, utils.EzPickle): def __init__(self, steps_before_reward=200, n_links=5, balance=False): utils.EzPickle.__init__(**locals()) @@ -31,7 +31,7 @@ class ALRReacherEnv(mujoco_env.MujocoEnv, utils.EzPickle): else: raise ValueError(f"Invalid number of links {n_links}, only 5 or 7 allowed.") - mujoco_env.MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", file_name), 2) + MujocoEnv.__init__(self, os.path.join(os.path.dirname(__file__), "assets", file_name), 2) def step(self, a): self._steps += 1 diff --git a/alr_envs/utils/legacy/detpmp_env_wrapper.py b/alr_envs/utils/legacy/detpmp_env_wrapper.py deleted file mode 100644 index c667abf..0000000 --- a/alr_envs/utils/legacy/detpmp_env_wrapper.py +++ /dev/null @@ -1,88 +0,0 @@ -from alr_envs.utils.policies import get_policy_class -from mp_lib import det_promp -import numpy as np -import gym - - -class DetPMPEnvWrapper(gym.Wrapper): - def __init__(self, - env, - num_dof, - num_basis, - width, - off=0.01, - start_pos=None, - duration=1, - dt=0.01, - post_traj_time=0., - policy_type=None, - weights_scale=1, - zero_start=False, - zero_goal=False, - ): - super(DetPMPEnvWrapper, self).__init__(env) - self.num_dof = num_dof - self.num_basis = num_basis - self.dim = num_dof * num_basis - self.pmp = det_promp.DeterministicProMP(n_basis=num_basis, n_dof=num_dof, width=width, off=off, - zero_start=zero_start, zero_goal=zero_goal) - weights = np.zeros(shape=(num_basis, num_dof)) - self.pmp.set_weights(duration, weights) - self.weights_scale = weights_scale - - self.duration = duration - self.dt = dt - self.post_traj_steps = int(post_traj_time / dt) - - self.start_pos = start_pos - self.zero_start = zero_start - - policy_class = get_policy_class(policy_type) - self.policy = policy_class(env) - - def __call__(self, params, contexts=None): - params = np.atleast_2d(params) - rewards = [] - infos = [] - for p, c in zip(params, contexts): - reward, info = self.rollout(p, c) - rewards.append(reward) - infos.append(info) - - return np.array(rewards), infos - - def rollout(self, params, context=None, render=False): - """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step""" - params = np.reshape(params, newshape=(self.num_basis, self.num_dof)) * self.weights_scale - self.pmp.set_weights(self.duration, params) - t, des_pos, des_vel, des_acc = self.pmp.compute_trajectory(1 / self.dt, 1.) - if self.zero_start: - des_pos += self.start_pos[None, :] - - if self.post_traj_steps > 0: - des_pos = np.vstack([des_pos, np.tile(des_pos[-1, :], [self.post_traj_steps, 1])]) - des_vel = np.vstack([des_vel, np.zeros(shape=(self.post_traj_steps, self.num_dof))]) - - self._trajectory = des_pos - self._velocity = des_vel - - rews = [] - infos = [] - - self.env.configure(context) - self.env.reset() - - for t, pos_vel in enumerate(zip(des_pos, des_vel)): - ac = self.policy.get_action(pos_vel[0], pos_vel[1]) - obs, rew, done, info = self.env.step(ac) - rews.append(rew) - infos.append(info) - if render: - self.env.render(mode="human") - if done: - break - - reward = np.sum(rews) - - return reward, info - diff --git a/alr_envs/utils/legacy/dmp_async_vec_env.py b/alr_envs/utils/legacy/dmp_async_vec_env.py deleted file mode 100644 index 641e770..0000000 --- a/alr_envs/utils/legacy/dmp_async_vec_env.py +++ /dev/null @@ -1,173 +0,0 @@ -import gym -from gym.error import (AlreadyPendingCallError, NoAsyncCallError) -from gym.vector.utils import concatenate, create_empty_array -from gym.vector.async_vector_env import AsyncState -import numpy as np -import multiprocessing as mp -import sys - - -def _worker(index, env_fn, pipe, parent_pipe, shared_memory, error_queue): - assert shared_memory is None - env = env_fn() - parent_pipe.close() - try: - while True: - command, data = pipe.recv() - if command == 'reset': - observation = env.reset() - pipe.send((observation, True)) - elif command == 'step': - observation, reward, done, info = env.step(data) - if done: - observation = env.reset() - pipe.send(((observation, reward, done, info), True)) - elif command == 'rollout': - rewards = [] - infos = [] - for p, c in zip(*data): - reward, info = env.rollout(p, c) - rewards.append(reward) - infos.append(info) - pipe.send(((rewards, infos), (True, ) * len(rewards))) - elif command == 'seed': - env.seed(data) - pipe.send((None, True)) - elif command == 'close': - env.close() - pipe.send((None, True)) - break - elif command == 'idle': - pipe.send((None, True)) - elif command == '_check_observation_space': - pipe.send((data == env.observation_space, True)) - else: - raise RuntimeError('Received unknown command `{0}`. Must ' - 'be one of {`reset`, `step`, `seed`, `close`, ' - '`_check_observation_space`}.'.format(command)) - except (KeyboardInterrupt, Exception): - error_queue.put((index,) + sys.exc_info()[:2]) - pipe.send((None, False)) - finally: - env.close() - - -class DmpAsyncVectorEnv(gym.vector.AsyncVectorEnv): - def __init__(self, env_fns, n_samples, observation_space=None, action_space=None, - shared_memory=False, copy=True, context="spawn", daemon=True, worker=_worker): - super(DmpAsyncVectorEnv, self).__init__(env_fns, - observation_space=observation_space, - action_space=action_space, - shared_memory=shared_memory, - copy=copy, - context=context, - daemon=daemon, - worker=worker) - - # we need to overwrite the number of samples as we may sample more than num_envs - self.observations = create_empty_array(self.single_observation_space, - n=n_samples, - fn=np.zeros) - - def __call__(self, params, contexts=None): - return self.rollout(params, contexts) - - def rollout_async(self, params, contexts): - """ - Parameters - ---------- - params : iterable of samples from `action_space` - List of actions. - """ - self._assert_is_running() - if self._state != AsyncState.DEFAULT: - raise AlreadyPendingCallError('Calling `rollout_async` while waiting ' - 'for a pending call to `{0}` to complete.'.format( - self._state.value), self._state.value) - - params = np.atleast_2d(params) - split_params = np.array_split(params, np.minimum(len(params), self.num_envs)) - if contexts is None: - split_contexts = np.array_split([None, ] * len(params), np.minimum(len(params), self.num_envs)) - else: - split_contexts = np.array_split(contexts, np.minimum(len(contexts), self.num_envs)) - - assert np.all([len(p) == len(c) for p, c in zip(split_params, split_contexts)]) - for pipe, param, context in zip(self.parent_pipes, split_params, split_contexts): - pipe.send(('rollout', (param, context))) - for pipe in self.parent_pipes[len(split_params):]: - pipe.send(('idle', None)) - self._state = AsyncState.WAITING_ROLLOUT - - def rollout_wait(self, timeout=None): - """ - Parameters - ---------- - timeout : int or float, optional - Number of seconds before the call to `step_wait` times out. If - `None`, the call to `step_wait` never times out. - - Returns - ------- - observations : sample from `observation_space` - A batch of observations from the vectorized environment. - - rewards : `np.ndarray` instance (dtype `np.float_`) - A vector of rewards from the vectorized environment. - - dones : `np.ndarray` instance (dtype `np.bool_`) - A vector whose entries indicate whether the episode has ended. - - infos : list of dict - A list of auxiliary diagnostic information. - """ - self._assert_is_running() - if self._state != AsyncState.WAITING_ROLLOUT: - raise NoAsyncCallError('Calling `rollout_wait` without any prior call ' - 'to `rollout_async`.', AsyncState.WAITING_ROLLOUT.value) - - if not self._poll(timeout): - self._state = AsyncState.DEFAULT - raise mp.TimeoutError('The call to `rollout_wait` has timed out after ' - '{0} second{1}.'.format(timeout, 's' if timeout > 1 else '')) - - results, successes = zip(*[pipe.recv() for pipe in self.parent_pipes]) - results = [r for r in results if r is not None] - self._raise_if_errors(successes) - self._state = AsyncState.DEFAULT - - rewards, infos = [_flatten_list(r) for r in zip(*results)] - - # for now, we ignore the observations and only return the rewards - - # if not self.shared_memory: - # self.observations = concatenate(observations_list, self.observations, - # self.single_observation_space) - - # return (deepcopy(self.observations) if self.copy else self.observations, - # np.array(rewards), np.array(dones, dtype=np.bool_), infos) - - return np.array(rewards), infos - - def rollout(self, actions, contexts): - self.rollout_async(actions, contexts) - return self.rollout_wait() - - -def _flatten_obs(obs): - assert isinstance(obs, (list, tuple)) - assert len(obs) > 0 - - if isinstance(obs[0], dict): - keys = obs[0].keys() - return {k: np.stack([o[k] for o in obs]) for k in keys} - else: - return np.stack(obs) - - -def _flatten_list(l): - assert isinstance(l, (list, tuple)) - assert len(l) > 0 - assert all([len(l_) > 0 for l_ in l]) - - return [l__ for l_ in l for l__ in l_] diff --git a/alr_envs/utils/legacy/dmp_env_wrapper.py b/alr_envs/utils/legacy/dmp_env_wrapper.py deleted file mode 100644 index 6835d80..0000000 --- a/alr_envs/utils/legacy/dmp_env_wrapper.py +++ /dev/null @@ -1,125 +0,0 @@ -from alr_envs.utils.policies import get_policy_class -from mp_lib.phase import ExpDecayPhaseGenerator -from mp_lib.basis import DMPBasisGenerator -from mp_lib import dmps -import numpy as np -import gym - - -class DmpEnvWrapper(gym.Wrapper): - def __init__(self, - env, - num_dof, - num_basis, - start_pos=None, - final_pos=None, - duration=1, - dt=0.01, - alpha_phase=2, - bandwidth_factor=3, - learn_goal=False, - post_traj_time=0., - policy_type=None, - weights_scale=1., - goal_scale=1., - ): - super(DmpEnvWrapper, self).__init__(env) - self.num_dof = num_dof - self.num_basis = num_basis - self.dim = num_dof * num_basis - if learn_goal: - self.dim += num_dof - self.learn_goal = learn_goal - self.duration = duration # seconds - time_steps = int(duration / dt) - self.t = np.linspace(0, duration, time_steps) - self.post_traj_steps = int(post_traj_time / dt) - - phase_generator = ExpDecayPhaseGenerator(alpha_phase=alpha_phase, duration=duration) - basis_generator = DMPBasisGenerator(phase_generator, - duration=duration, - num_basis=self.num_basis, - basis_bandwidth_factor=bandwidth_factor) - - self.dmp = dmps.DMP(num_dof=num_dof, - basis_generator=basis_generator, - phase_generator=phase_generator, - num_time_steps=time_steps, - dt=dt - ) - - self.dmp.dmp_start_pos = start_pos.reshape((1, num_dof)) - - dmp_weights = np.zeros((num_basis, num_dof)) - if learn_goal: - dmp_goal_pos = np.zeros(num_dof) - else: - dmp_goal_pos = final_pos - - self.dmp.set_weights(dmp_weights, dmp_goal_pos) - self.weights_scale = weights_scale - self.goal_scale = goal_scale - - policy_class = get_policy_class(policy_type) - self.policy = policy_class(env) - - def __call__(self, params, contexts=None): - params = np.atleast_2d(params) - rewards = [] - infos = [] - for p, c in zip(params, contexts): - reward, info = self.rollout(p, c) - rewards.append(reward) - infos.append(info) - - return np.array(rewards), infos - - def goal_and_weights(self, params): - if len(params.shape) > 1: - assert params.shape[1] == self.dim - else: - assert len(params) == self.dim - params = np.reshape(params, [1, self.dim]) - - if self.learn_goal: - goal_pos = params[0, -self.num_dof:] - weight_matrix = np.reshape(params[:, :-self.num_dof], [self.num_basis, self.num_dof]) - else: - goal_pos = self.dmp.dmp_goal_pos.flatten() - assert goal_pos is not None - weight_matrix = np.reshape(params, [self.num_basis, self.num_dof]) - - return goal_pos * self.goal_scale, weight_matrix * self.weights_scale - - def rollout(self, params, context=None, render=False): - """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step""" - goal_pos, weight_matrix = self.goal_and_weights(params) - self.dmp.set_weights(weight_matrix, goal_pos) - trajectory, velocity = self.dmp.reference_trajectory(self.t) - - if self.post_traj_steps > 0: - trajectory = np.vstack([trajectory, np.tile(trajectory[-1, :], [self.post_traj_steps, 1])]) - velocity = np.vstack([velocity, np.zeros(shape=(self.post_traj_steps, self.num_dof))]) - - self._trajectory = trajectory - self._velocity = velocity - - rews = [] - infos = [] - - self.env.configure(context) - self.env.reset() - - for t, pos_vel in enumerate(zip(trajectory, velocity)): - ac = self.policy.get_action(pos_vel[0], pos_vel[1]) - obs, rew, done, info = self.env.step(ac) - rews.append(rew) - infos.append(info) - if render: - self.env.render(mode="human") - if done: - break - - reward = np.sum(rews) - - return reward, info diff --git a/alr_envs/utils/legacy/dmp_env_wrapper_example.py b/alr_envs/utils/legacy/dmp_env_wrapper_example.py deleted file mode 100644 index d2edae5..0000000 --- a/alr_envs/utils/legacy/dmp_env_wrapper_example.py +++ /dev/null @@ -1,28 +0,0 @@ -from alr_envs.utils.legacy.utils import make_holereacher_env -import numpy as np - -if __name__ == "__main__": - n_samples = 1 - n_cpus = 4 - dim = 30 - - # env = DmpAsyncVectorEnv([make_viapointreacher_env(i) for i in range(n_cpus)], - # n_samples=n_samples) - - test_env = make_holereacher_env(0)() - - # params = np.random.randn(n_samples, dim) - params = np.array([[1.386102, -3.29980525, 4.70402733, 1.3966668, 0.73774902, - 3.14676681, -4.98644416, 6.20303193, 1.30502127, -0.09330522, - 7.62656797, -5.76893033, 3.4706711, -0.6944142, -3.33442788, - 12.31421548, -0.72760271, -6.9090723, 7.02903814, -8.7236836, - 1.4805914, 0.53185824, -5.46626893, 0.69692163, 13.58472666, - 0.77199316, 2.02906724, -3.0203244, -1.00533159, -0.57417351]]) - - # params = np.hstack([50 * np.random.randn(n_samples, 25), np.tile(np.array([np.pi/2, -np.pi/4, -np.pi/4, -np.pi/4, -np.pi/4]), [n_samples, 1])]) - - rew, info = test_env.rollout(params, render=True) - print(rew) - - # out = env(params) - # print(out) diff --git a/alr_envs/utils/legacy/dmp_pd_control_example.py b/alr_envs/utils/legacy/dmp_pd_control_example.py deleted file mode 100644 index 3b713f3..0000000 --- a/alr_envs/utils/legacy/dmp_pd_control_example.py +++ /dev/null @@ -1,28 +0,0 @@ -from alr_envs.mujoco.ball_in_a_cup.utils import make_simple_dmp_env -import numpy as np - -if __name__ == "__main__": - - dim = 15 - n_cpus = 4 - - # n_samples = 10 - # - # vec_env = DmpAsyncVectorEnv([make_simple_env(i) for i in range(n_cpus)], - # n_samples=n_samples) - # - # params = np.tile(1 * np.random.randn(n_samples, dim), (10, 1)) - # - # rewards, infos = vec_env(params) - # print(rewards) - # - non_vec_env = make_simple_dmp_env(0, 0)() - - # params = 0.5 * np.random.randn(dim) - params = np.array([-2.63357598, -1.04950296, -0.44330737, 0.52950017, 4.29247739, - 4.52473661, -0.05685977, -0.76796851, 3.71540499, 1.22631059, - 2.20412438, 3.91588129, -0.12652723, -3.0788211 , 0.56204464]) - - out2 = non_vec_env.rollout(params, render=True ) - - print(out2) diff --git a/alr_envs/utils/legacy/utils.py b/alr_envs/utils/legacy/utils.py deleted file mode 100644 index c158cae..0000000 --- a/alr_envs/utils/legacy/utils.py +++ /dev/null @@ -1,156 +0,0 @@ -import alr_envs.classic_control.hole_reacher as hr -import alr_envs.classic_control.viapoint_reacher as vpr -from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper -from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper -import numpy as np - - -def make_viapointreacher_env(rank, seed=0): - """ - Utility function for multiprocessed env. - - :param env_id: (str) the environment ID - :param num_env: (int) the number of environments you wish to have in subprocesses - :param seed: (int) the initial seed for RNG - :param rank: (int) index of the subprocess - :returns a function that generates an environment - """ - - def _init(): - _env = vpr.ViaPointReacher(n_links=5, - allow_self_collision=False, - collision_penalty=1000) - - _env = DmpWrapper(_env, - num_dof=5, - num_basis=5, - duration=2, - alpha_phase=2.5, - dt=_env.dt, - start_pos=_env.start_pos, - learn_goal=False, - policy_type="velocity", - weights_scale=50) - _env.seed(seed + rank) - return _env - - return _init - - -def make_holereacher_env(rank, seed=0): - """ - Utility function for multiprocessed env. - - :param env_id: (str) the environment ID - :param num_env: (int) the number of environments you wish to have in subprocesses - :param seed: (int) the initial seed for RNG - :param rank: (int) index of the subprocess - :returns a function that generates an environment - """ - - def _init(): - _env = hr.HoleReacher(n_links=5, - allow_self_collision=False, - allow_wall_collision=False, - hole_width=0.25, - hole_depth=1, - hole_x=2, - collision_penalty=100) - - _env = DmpWrapper(_env, - num_dof=5, - num_basis=5, - duration=2, - bandwidth_factor=2, - dt=_env.dt, - learn_goal=True, - alpha_phase=2, - start_pos=_env.start_pos, - policy_type="velocity", - weights_scale=50, - goal_scale=0.1 - ) - - _env.seed(seed + rank) - return _env - - return _init - - -def make_holereacher_fix_goal_env(rank, seed=0): - """ - Utility function for multiprocessed env. - - :param env_id: (str) the environment ID - :param num_env: (int) the number of environments you wish to have in subprocesses - :param seed: (int) the initial seed for RNG - :param rank: (int) index of the subprocess - :returns a function that generates an environment - """ - - def _init(): - _env = hr.HoleReacher(n_links=5, - allow_self_collision=False, - allow_wall_collision=False, - hole_width=0.15, - hole_depth=1, - hole_x=1, - collision_penalty=100) - - _env = DmpWrapper(_env, - num_dof=5, - num_basis=5, - duration=2, - dt=_env.dt, - learn_goal=False, - final_pos=np.array([2.02669572, -1.25966385, -1.51618198, -0.80946476, 0.02012344]), - alpha_phase=2, - start_pos=_env.start_pos, - policy_type="velocity", - weights_scale=50, - goal_scale=1 - ) - - _env.seed(seed + rank) - return _env - - return _init - - -def make_holereacher_env_pmp(rank, seed=0): - """ - Utility function for multiprocessed env. - - :param env_id: (str) the environment ID - :param num_env: (int) the number of environments you wish to have in subprocesses - :param seed: (int) the initial seed for RNG - :param rank: (int) index of the subprocess - :returns a function that generates an environment - """ - - def _init(): - _env = hr.HoleReacher(n_links=5, - allow_self_collision=False, - allow_wall_collision=False, - hole_width=0.15, - hole_depth=1, - hole_x=1, - collision_penalty=1000) - - _env = DetPMPWrapper(_env, - num_dof=5, - num_basis=5, - width=0.02, - policy_type="velocity", - start_pos=_env.start_pos, - duration=2, - post_traj_time=0, - dt=_env.dt, - weights_scale=0.2, - zero_start=True, - zero_goal=False - ) - _env.seed(seed + rank) - return _env - - return _init diff --git a/alr_envs/utils/make_env_helpers.py b/alr_envs/utils/make_env_helpers.py index c0e55b4..d455496 100644 --- a/alr_envs/utils/make_env_helpers.py +++ b/alr_envs/utils/make_env_helpers.py @@ -1,5 +1,5 @@ -from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper -from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper +from alr_envs.utils.mps.dmp_wrapper import DmpWrapper +from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper import gym from gym.vector.utils import write_to_shared_memory import sys diff --git a/alr_envs/utils/mp_env_async_sampler.py b/alr_envs/utils/mp_env_async_sampler.py index 344cb37..e935ba6 100644 --- a/alr_envs/utils/mp_env_async_sampler.py +++ b/alr_envs/utils/mp_env_async_sampler.py @@ -4,8 +4,8 @@ import numpy as np from _collections import defaultdict -def make_env(env_id, rank, seed=0): - env = gym.make(env_id) +def make_env(env_id, rank, seed=0, **env_kwargs): + env = gym.make(env_id, **env_kwargs) env.seed(seed + rank) return lambda: env @@ -45,9 +45,9 @@ class AlrMpEnvSampler: An asynchronous sampler for non contextual MPWrapper environments. A sampler object can be called with a set of parameters and returns the corresponding final obs, rewards, dones and info dicts. """ - def __init__(self, env_id, num_envs, seed=0): + def __init__(self, env_id, num_envs, seed=0, **env_kwargs): self.num_envs = num_envs - self.env = AsyncVectorEnv([make_env(env_id, seed, i) for i in range(num_envs)]) + self.env = AsyncVectorEnv([make_env(env_id, seed, i, **env_kwargs) for i in range(num_envs)]) def __call__(self, params): params = np.atleast_2d(params) @@ -56,6 +56,7 @@ class AlrMpEnvSampler: vals = defaultdict(list) for p in split_params: + self.env.reset() obs, reward, done, info = self.env.step(p) vals['obs'].append(obs) vals['reward'].append(reward) @@ -67,6 +68,37 @@ class AlrMpEnvSampler: _flatten_list(vals['done'])[:n_samples], _flatten_list(vals['info'])[:n_samples] +class AlrContextualMpEnvSampler: + """ + An asynchronous sampler for contextual MPWrapper environments. A sampler object can be called with a set of + parameters and returns the corresponding final obs, rewards, dones and info dicts. + """ + def __init__(self, env_id, num_envs, seed=0, **env_kwargs): + self.num_envs = num_envs + self.env = AsyncVectorEnv([make_env(env_id, seed, i, **env_kwargs) for i in range(num_envs)]) + + def __call__(self, dist, n_samples): + + repeat = int(np.ceil(n_samples / self.env.num_envs)) + vals = defaultdict(list) + for i in range(repeat): + new_contexts = self.env.reset() + vals['new_contexts'].append(new_contexts) + new_samples, new_contexts = dist.sample(new_contexts) + vals['new_samples'].append(new_samples) + + obs, reward, done, info = self.env.step(new_samples) + vals['obs'].append(obs) + vals['reward'].append(reward) + vals['done'].append(done) + vals['info'].append(info) + + # do not return values above threshold + return np.vstack(vals['new_samples'])[:n_samples], np.vstack(vals['new_contexts'])[:n_samples], \ + np.vstack(vals['obs'])[:n_samples], np.hstack(vals['reward'])[:n_samples], \ + _flatten_list(vals['done'])[:n_samples], _flatten_list(vals['info'])[:n_samples] + + if __name__ == "__main__": env_name = "alr_envs:ALRBallInACupSimpleDMP-v0" n_cpu = 8 diff --git a/alr_envs/utils/legacy/__init__.py b/alr_envs/utils/mps/__init__.py similarity index 100% rename from alr_envs/utils/legacy/__init__.py rename to alr_envs/utils/mps/__init__.py diff --git a/alr_envs/utils/wrapper/detpmp_wrapper.py b/alr_envs/utils/mps/detpmp_wrapper.py similarity index 59% rename from alr_envs/utils/wrapper/detpmp_wrapper.py rename to alr_envs/utils/mps/detpmp_wrapper.py index 62b93d5..3f6d1ee 100644 --- a/alr_envs/utils/wrapper/detpmp_wrapper.py +++ b/alr_envs/utils/mps/detpmp_wrapper.py @@ -2,26 +2,27 @@ import gym import numpy as np from mp_lib import det_promp -from alr_envs.utils.wrapper.mp_wrapper import MPWrapper +from alr_envs.utils.mps.mp_environments import MPEnv +from alr_envs.utils.mps.mp_wrapper import MPWrapper class DetPMPWrapper(MPWrapper): - def __init__(self, env, num_dof, num_basis, width, start_pos=None, duration=1, dt=0.01, post_traj_time=0., - policy_type=None, weights_scale=1, zero_start=False, zero_goal=False, **mp_kwargs): - # self.duration = duration # seconds + def __init__(self, env: MPEnv, num_dof: int, num_basis: int, width: int, duration: int = 1, dt: float = 0.01, + post_traj_time: float = 0., policy_type: str = None, weights_scale: float = 1., + zero_start: bool = False, zero_goal: bool = False, **mp_kwargs): + self.duration = duration # seconds - super().__init__(env, num_dof, duration, dt, post_traj_time, policy_type, weights_scale, - num_basis=num_basis, width=width, start_pos=start_pos, zero_start=zero_start, - zero_goal=zero_goal) + super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, num_basis=num_basis, + width=width, zero_start=zero_start, zero_goal=zero_goal, **mp_kwargs) + + self.dt = dt action_bounds = np.inf * np.ones((self.mp.n_basis * self.mp.n_dof)) self.action_space = gym.spaces.Box(low=-action_bounds, high=action_bounds, dtype=np.float32) - self.start_pos = start_pos - self.dt = dt def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5, width: float = None, - start_pos: np.ndarray = None, zero_start: bool = False, zero_goal: bool = False): + zero_start: bool = False, zero_goal: bool = False): pmp = det_promp.DeterministicProMP(n_basis=num_basis, n_dof=num_dof, width=width, off=0.01, zero_start=zero_start, zero_goal=zero_goal) diff --git a/alr_envs/utils/wrapper/dmp_wrapper.py b/alr_envs/utils/mps/dmp_wrapper.py similarity index 59% rename from alr_envs/utils/wrapper/dmp_wrapper.py rename to alr_envs/utils/mps/dmp_wrapper.py index 8f94227..6bc5bc6 100644 --- a/alr_envs/utils/wrapper/dmp_wrapper.py +++ b/alr_envs/utils/mps/dmp_wrapper.py @@ -1,17 +1,18 @@ -from mp_lib.phase import ExpDecayPhaseGenerator -from mp_lib.basis import DMPBasisGenerator -from mp_lib import dmps -import numpy as np import gym +import numpy as np +from mp_lib import dmps +from mp_lib.basis import DMPBasisGenerator +from mp_lib.phase import ExpDecayPhaseGenerator -from alr_envs.utils.wrapper.mp_wrapper import MPWrapper +from alr_envs.utils.mps.mp_environments import MPEnv +from alr_envs.utils.mps.mp_wrapper import MPWrapper class DmpWrapper(MPWrapper): - def __init__(self, env: gym.Env, num_dof: int, num_basis: int, start_pos: np.ndarray = None, - final_pos: np.ndarray = None, duration: int = 1, alpha_phase: float = 2., dt: float = None, - learn_goal: bool = False, return_to_start: bool = False, post_traj_time: float = 0., + def __init__(self, env: MPEnv, num_dof: int, num_basis: int, + duration: int = 1, alpha_phase: float = 2., dt: float = None, + learn_goal: bool = False, post_traj_time: float = 0., weights_scale: float = 1., goal_scale: float = 1., bandwidth_factor: float = 3., policy_type: str = None, render_mode: str = None): @@ -21,8 +22,6 @@ class DmpWrapper(MPWrapper): env: num_dof: num_basis: - start_pos: - final_pos: duration: alpha_phase: dt: @@ -35,25 +34,17 @@ class DmpWrapper(MPWrapper): self.learn_goal = learn_goal dt = env.dt if hasattr(env, "dt") else dt assert dt is not None - start_pos = start_pos if start_pos is not None else env.start_pos if hasattr(env, "start_pos") else None - assert start_pos is not None - if learn_goal: - final_pos = np.zeros_like(start_pos) # arbitrary, will be learned - else: - final_pos = final_pos if final_pos is not None else start_pos if return_to_start else None - assert final_pos is not None self.t = np.linspace(0, duration, int(duration / dt)) self.goal_scale = goal_scale - super().__init__(env, num_dof, duration, dt, post_traj_time, policy_type, weights_scale, render_mode, - num_basis=num_basis, start_pos=start_pos, final_pos=final_pos, alpha_phase=alpha_phase, - bandwidth_factor=bandwidth_factor) + super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, render_mode, + num_basis=num_basis, alpha_phase=alpha_phase, bandwidth_factor=bandwidth_factor) action_bounds = np.inf * np.ones((np.prod(self.mp.dmp_weights.shape) + (num_dof if learn_goal else 0))) self.action_space = gym.spaces.Box(low=-action_bounds, high=action_bounds, dtype=np.float32) - def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5, start_pos: np.ndarray = None, - final_pos: np.ndarray = None, alpha_phase: float = 2., bandwidth_factor: float = 3.): + def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5, alpha_phase: float = 2., + bandwidth_factor: int = 3): phase_generator = ExpDecayPhaseGenerator(alpha_phase=alpha_phase, duration=duration) basis_generator = DMPBasisGenerator(phase_generator, duration=duration, num_basis=num_basis, @@ -62,12 +53,6 @@ class DmpWrapper(MPWrapper): dmp = dmps.DMP(num_dof=num_dof, basis_generator=basis_generator, phase_generator=phase_generator, num_time_steps=int(duration / dt), dt=dt) - dmp.dmp_start_pos = start_pos.reshape((1, num_dof)) - - weights = np.zeros((num_basis, num_dof)) - goal_pos = np.zeros(num_dof) if self.learn_goal else final_pos - - dmp.set_weights(weights, goal_pos) return dmp def goal_and_weights(self, params): @@ -77,16 +62,15 @@ class DmpWrapper(MPWrapper): if self.learn_goal: goal_pos = params[0, -self.mp.num_dimensions:] # [num_dof] params = params[:, :-self.mp.num_dimensions] # [1,num_dof] - # weight_matrix = np.reshape(params[:, :-self.num_dof], [self.num_basis, self.num_dof]) else: - goal_pos = self.mp.dmp_goal_pos.flatten() + goal_pos = self.env.goal_pos assert goal_pos is not None - # weight_matrix = np.reshape(params, [self.num_basis, self.num_dof]) - weight_matrix = np.reshape(params, self.mp.dmp_weights.shape) + weight_matrix = np.reshape(params, self.mp.dmp_weights.shape) # [num_basis, num_dof] return goal_pos * self.goal_scale, weight_matrix * self.weights_scale def mp_rollout(self, action): + self.mp.dmp_start_pos = self.env.start_pos goal_pos, weight_matrix = self.goal_and_weights(action) self.mp.set_weights(weight_matrix, goal_pos) return self.mp.reference_trajectory(self.t) diff --git a/alr_envs/utils/mps/mp_environments.py b/alr_envs/utils/mps/mp_environments.py new file mode 100644 index 0000000..f397491 --- /dev/null +++ b/alr_envs/utils/mps/mp_environments.py @@ -0,0 +1,33 @@ +from abc import abstractmethod +from typing import Union + +import gym +import numpy as np + + +class MPEnv(gym.Env): + + @property + @abstractmethod + def active_obs(self): + """Returns boolean mask for each observation entry + whether the observation is returned for the contextual case or not. + This effectively allows to filter unwanted or unnecessary observations from the full step-based case. + """ + return np.ones(self.observation_space.shape, dtype=bool) + + @property + @abstractmethod + def start_pos(self) -> Union[float, int, np.ndarray]: + """ + Returns the starting position of the joints + """ + raise NotImplementedError() + + @property + def goal_pos(self) -> Union[float, int, np.ndarray]: + """ + Returns the current final position of the joints for the MP. + By default this returns the starting position. + """ + return self.start_pos diff --git a/alr_envs/utils/wrapper/mp_wrapper.py b/alr_envs/utils/mps/mp_wrapper.py similarity index 68% rename from alr_envs/utils/wrapper/mp_wrapper.py rename to alr_envs/utils/mps/mp_wrapper.py index 34f0440..4c92806 100644 --- a/alr_envs/utils/wrapper/mp_wrapper.py +++ b/alr_envs/utils/mps/mp_wrapper.py @@ -1,32 +1,24 @@ from abc import ABC, abstractmethod -from collections import defaultdict import gym import numpy as np +from alr_envs.utils.mps.mp_environments import MPEnv from alr_envs.utils.policies import get_policy_class class MPWrapper(gym.Wrapper, ABC): - def __init__(self, - env: gym.Env, - num_dof: int, - duration: int = 1, - dt: float = None, - post_traj_time: float = 0., - policy_type: str = None, - weights_scale: float = 1., - render_mode: str = None, - **mp_kwargs - ): + def __init__(self, env: MPEnv, num_dof: int, dt: float, duration: int = 1, post_traj_time: float = 0., + policy_type: str = None, weights_scale: float = 1., render_mode: str = None, **mp_kwargs): super().__init__(env) - # self.num_dof = num_dof - # self.num_basis = num_basis - # self.duration = duration # seconds + # adjust observation space to reduce version + obs_sp = self.env.observation_space + self.observation_space = gym.spaces.Box(low=obs_sp.low[self.env.active_obs], + high=obs_sp.high[self.env.active_obs], + dtype=obs_sp.dtype) - # dt = env.dt if hasattr(env, "dt") else dt assert dt is not None # this should never happen as MPWrapper is a base class self.post_traj_steps = int(post_traj_time / dt) @@ -40,8 +32,11 @@ class MPWrapper(gym.Wrapper, ABC): self.render_mode = render_mode self.render_kwargs = {} - # TODO: not yet final + # TODO: @Max I think this should not be in this class, this functionality should be part of your sampler. def __call__(self, params, contexts=None): + """ + Can be used to provide a batch of parameter sets + """ params = np.atleast_2d(params) obs = [] rewards = [] @@ -61,6 +56,9 @@ class MPWrapper(gym.Wrapper, ABC): def configure(self, context): self.env.configure(context) + def reset(self): + return self.env.reset()[self.env.active_obs] + def step(self, action: np.ndarray): """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step""" trajectory, velocity = self.mp_rollout(action) @@ -73,14 +71,9 @@ class MPWrapper(gym.Wrapper, ABC): # self._velocity = velocity rewards = 0 - # infos = defaultdict(list) - - # TODO: @Max Why do we need this configure, states should be part of the model - # TODO: Ask Onur if the context distribution needs to be outside the environment - # TODO: For now create a new env with each context - # self.env.configure(context) - obs = self.env.reset() info = {} + # create random obs as the reset function is called externally + obs = self.env.observation_space.sample() for t, pos_vel in enumerate(zip(trajectory, velocity)): ac = self.policy.get_action(pos_vel[0], pos_vel[1]) @@ -94,7 +87,7 @@ class MPWrapper(gym.Wrapper, ABC): break done = True - return obs, rewards, done, info + return obs[self.env.active_obs], rewards, done, info def render(self, mode='human', **kwargs): """Only set render options here, such that they can be used during the rollout. @@ -102,18 +95,6 @@ class MPWrapper(gym.Wrapper, ABC): self.render_mode = mode self.render_kwargs = kwargs - # def __call__(self, actions): - # return self.step(actions) - # params = np.atleast_2d(params) - # rewards = [] - # infos = [] - # for p, c in zip(params, contexts): - # reward, info = self.rollout(p, c) - # rewards.append(reward) - # infos.append(info) - # - # return np.array(rewards), infos - @abstractmethod def mp_rollout(self, action): """ diff --git a/alr_envs/utils/wrapper/__init__.py b/alr_envs/utils/wrapper/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/example.py b/example.py index 94da23c..166f38f 100644 --- a/example.py +++ b/example.py @@ -46,7 +46,7 @@ def example_dmp(): obs = env.reset() -def example_async(n_cpu=4, seed=int('533D', 16)): +def example_async(env_id="alr_envs:HoleReacherDMP-v0", n_cpu=4, seed=int('533D', 16)): def make_env(env_id, seed, rank): env = gym.make(env_id) env.seed(seed + rank) @@ -73,7 +73,7 @@ def example_async(n_cpu=4, seed=int('533D', 16)): # do not return values above threshold return (*map(lambda v: np.stack(v)[:n_samples], vals.values()),) - envs = gym.vector.AsyncVectorEnv([make_env("alr_envs:HoleReacherDMP-v0", seed, i) for i in range(n_cpu)]) + envs = gym.vector.AsyncVectorEnv([make_env(env_id, seed, i) for i in range(n_cpu)]) obs = envs.reset() print(sample(envs, 16)) @@ -82,6 +82,6 @@ def example_async(n_cpu=4, seed=int('533D', 16)): if __name__ == '__main__': # example_mujoco() # example_dmp() - # example_async() - env = gym.make("alr_envs:HoleReacherDMP-v0", context=0.1) - print() \ No newline at end of file + example_async("alr_envs:LongSimpleReacherDMP-v0", 4) + # env = gym.make("alr_envs:HoleReacherDMP-v0", context=0.1) + # env = gym.make("alr_envs:HoleReacherDMP-v1")