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Maximilian Huettenrauch 2021-05-17 12:49:15 +02:00
commit b39104a449
16 changed files with 671 additions and 604 deletions
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149
alr_envs/__init__.py
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@ -1,7 +1,8 @@
from gym.envs.registration import register from gym.envs.registration import register
from alr_envs.stochastic_search.functions.f_rosenbrock import Rosenbrock 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 # 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( register(
id='ALRBallInACupSimple-v0', id='ALRBallInACupSimple-v0',
entry_point='alr_envs.mujoco:ALRBallInACupEnv', entry_point='alr_envs.mujoco:ALRBallInACupEnv',
@ -101,15 +113,7 @@ register(
# Classic control # Classic control
register( ## Simple Reacher
id='Balancing-v0',
entry_point='alr_envs.mujoco:BalancingEnv',
max_episode_steps=200,
kwargs={
"n_links": 5,
}
)
register( register(
id='SimpleReacher-v0', id='SimpleReacher-v0',
entry_point='alr_envs.classic_control:SimpleReacherEnv', entry_point='alr_envs.classic_control:SimpleReacherEnv',
@ -129,25 +133,6 @@ register(
} }
) )
register(
id='EpisodicSimpleReacher-v0',
entry_point='alr_envs.classic_control:EpisodicSimpleReacherEnv',
max_episode_steps=200,
kwargs={
"n_links": 2,
}
)
register(
id='EpisodicSimpleReacher-v1',
entry_point='alr_envs.classic_control:EpisodicSimpleReacherEnv',
max_episode_steps=200,
kwargs={
"n_links": 2,
"random_start": False
}
)
register( register(
id='LongSimpleReacher-v0', id='LongSimpleReacher-v0',
entry_point='alr_envs.classic_control:SimpleReacherEnv', entry_point='alr_envs.classic_control:SimpleReacherEnv',
@ -157,6 +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( register(
id='ViaPointReacher-v0', id='ViaPointReacher-v0',
entry_point='alr_envs.classic_control.viapoint_reacher:ViaPointReacher', entry_point='alr_envs.classic_control.viapoint_reacher:ViaPointReacher',
@ -168,27 +165,45 @@ register(
} }
) )
## Hole Reacher
register( register(
id='HoleReacher-v0', 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, max_episode_steps=200,
kwargs={ kwargs={
"n_links": 5, "n_links": 5,
"allow_self_collision": False, "allow_self_collision": False,
"allow_wall_collision": False, "allow_wall_collision": False,
"hole_width": 0.25, "hole_width": None,
"hole_depth": 1, "hole_depth": 1,
"hole_x": 2, "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, "collision_penalty": 100,
} }
) )
register( register(
id='HoleReacher-v2', id='HoleReacher-v2',
entry_point='alr_envs.classic_control.hole_reacher_v2:HoleReacher', entry_point='alr_envs.classic_control.hole_reacher:HoleReacherEnv',
max_episode_steps=200, max_episode_steps=200,
kwargs={ kwargs={
"n_links": 5, "n_links": 5,
"random_start": False,
"allow_self_collision": False, "allow_self_collision": False,
"allow_wall_collision": False, "allow_wall_collision": False,
"hole_width": 0.25, "hole_width": 0.25,
@ -199,38 +214,24 @@ register(
) )
# MP environments # MP environments
reacher_envs = ["SimpleReacher-v0", "SimpleReacher-v1", "LongSimpleReacher-v0", "LongSimpleReacher-v1"]
register( for env in reacher_envs:
id='SimpleReacherDMP-v0', name = env.split("-")
entry_point='alr_envs.utils.make_env_helpers:make_dmp_env', register(
# max_episode_steps=1, id=f'{name[0]}DMP-{name[1]}',
kwargs={ entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
"name": "alr_envs:EpisodicSimpleReacher-v0", # max_episode_steps=1,
"num_dof": 2, kwargs={
"num_basis": 5, "name": f"alr_envs:{env}",
"duration": 2, "num_dof": 2 if "long" not in env.lower() else 5 ,
"alpha_phase": 2, "num_basis": 5,
"learn_goal": True, "duration": 2,
"policy_type": "velocity", "alpha_phase": 2,
"weights_scale": 50, "learn_goal": True,
} "policy_type": "velocity",
) "weights_scale": 50,
}
register( )
id='SimpleReacherDMP-v1',
entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
# max_episode_steps=1,
kwargs={
"name": "alr_envs:EpisodicSimpleReacher-v1",
"num_dof": 2,
"num_basis": 5,
"duration": 2,
"alpha_phase": 2,
"learn_goal": True,
"policy_type": "velocity",
"weights_scale": 50,
}
)
register( register(
id='ViaPointReacherDMP-v0', id='ViaPointReacherDMP-v0',
@ -266,6 +267,24 @@ register(
} }
) )
register(
id='HoleReacherDMP-v1',
entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',
# max_episode_steps=1,
kwargs={
"name": "alr_envs:HoleReacher-v1",
"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( register(
id='HoleReacherDMP-v2', id='HoleReacherDMP-v2',
entry_point='alr_envs.utils.make_env_helpers:make_dmp_env', entry_point='alr_envs.utils.make_env_helpers:make_dmp_env',

2
alr_envs/classic_control/__init__.py
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@ -1,4 +1,4 @@
from alr_envs.classic_control.simple_reacher import SimpleReacherEnv from alr_envs.classic_control.simple_reacher import SimpleReacherEnv
from alr_envs.classic_control.episodic_simple_reacher import EpisodicSimpleReacherEnv from alr_envs.classic_control.episodic_simple_reacher import EpisodicSimpleReacherEnv
from alr_envs.classic_control.viapoint_reacher import ViaPointReacher 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

2
alr_envs/classic_control/episodic_simple_reacher.py
View File

@ -35,7 +35,7 @@ class EpisodicSimpleReacherEnv(SimpleReacherEnv):
def _get_obs(self): def _get_obs(self):
if self.random_start: if self.random_start:
theta = self._joint_angle theta = self._joint_angles
return np.hstack([ return np.hstack([
np.cos(theta), np.cos(theta),
np.sin(theta), np.sin(theta),

319
alr_envs/classic_control/hole_reacher.py
View File

@ -1,27 +1,36 @@
from typing import Union
import gym import gym
import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np
from gym.utils import seeding
from matplotlib import patches from matplotlib import patches
from alr_envs.classic_control.utils import check_self_collision 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, def __init__(self, n_links: int, hole_x: Union[None, float] = None, hole_depth: Union[None, float] = None,
allow_wall_collision=False, collision_penalty=1000): 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.n_links = n_links
self.link_lengths = np.ones((n_links, 1)) self.link_lengths = np.ones((n_links, 1))
# task self.random_start = random_start
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.bottom_center_of_hole = np.hstack([hole_x, -hole_depth]) # provided initial parameters
self.top_center_of_hole = np.hstack([hole_x, 0]) self._hole_x = hole_x # x-position of center of hole
self.left_wall_edge = np.hstack([hole_x - self.hole_width / 2, 0]) self._hole_width = hole_width # width of hole
self.right_wall_edge = np.hstack([hole_x + self.hole_width / 2, 0]) 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 # collision
self.allow_self_collision = allow_self_collision self.allow_self_collision = allow_self_collision
@ -32,95 +41,77 @@ class HoleReacher(gym.Env):
self._joints = None self._joints = None
self._joint_angles = None self._joint_angles = None
self._angle_velocity = None self._angle_velocity = None
self.start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)]) self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)])
self.start_vel = np.zeros(self.n_links) self._start_vel = np.zeros(self.n_links)
self.dt = 0.01 self.dt = 0.01
# self.time_limit = 2
action_bound = np.pi * np.ones((self.n_links,)) action_bound = np.pi * np.ones((self.n_links,))
state_bound = np.hstack([ state_bound = np.hstack([
[np.pi] * self.n_links, # cos [np.pi] * self.n_links, # cos
[np.pi] * self.n_links, # sin [np.pi] * self.n_links, # sin
[np.inf] * self.n_links, # velocity [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] * 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 TODO: Maybe
]) ])
self.action_space = gym.spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape) 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) 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 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 step(self, action: np.ndarray):
def init_qpos(self): """
return self.start_pos A single step with an action in joint velocity space
"""
@property self._angle_velocity = action
def end_effector(self): self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
return self._joints[self.n_links].T self._update_joints()
def configure(self, context): acc = (action - self._angle_velocity) / self.dt
pass 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): def reset(self):
self._joint_angles = self.start_pos if self.random_start:
self._angle_velocity = self.start_vel # 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._joints = np.zeros((self.n_links + 1, 2))
self._update_joints() self._update_joints()
self._steps = 0 self._steps = 0
return self._get_obs().copy() return self._get_obs().copy()
def step(self, action: np.ndarray): 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)
a single step with an action in joint velocity space 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)
vel = action # + 0.05 * np.random.randn(self.n_links) # TODO we do not want this right now.
acc = (vel - self._angle_velocity) / self.dt self._tmp_hole_depth = self.np_random.uniform(1, 1, 1) if self._hole_depth is None else np.copy(
self._angle_velocity = vel self._hole_depth)
self._joint_angles = self._joint_angles + self.dt * self._angle_velocity self._goal = np.hstack([self._tmp_hole_x, -self._tmp_hole_depth])
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 _update_joints(self): def _update_joints(self):
""" """
@ -128,7 +119,7 @@ class HoleReacher(gym.Env):
Returns: 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:, 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] self._joints[1:, 1] = self._joints[0, 1] + line_points_in_taskspace[:, -1, 1]
@ -142,48 +133,65 @@ class HoleReacher(gym.Env):
self_collision = True self_collision = True
if not self.allow_wall_collision: 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 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): def _get_obs(self):
theta = self._joint_angles theta = self._joint_angles
return np.hstack([ return np.hstack([
np.cos(theta), np.cos(theta),
np.sin(theta), np.sin(theta),
self._angle_velocity, 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 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] theta = self._joint_angles[:, None]
if num_points_per_link > 1: intermediate_points = np.linspace(0, 1, num_points_per_link) if num_points_per_link > 1 else 1
intermediate_points = np.linspace(0, 1, num_points_per_link)
else:
intermediate_points = 1
accumulated_theta = np.cumsum(theta, axis=0) accumulated_theta = np.cumsum(theta, axis=0)
end_effector = np.zeros(shape=(self.n_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 x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points
y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points
endeffector[0, :, 0] = x[0, :] end_effector[0, :, 0] = x[0, :]
endeffector[0, :, 1] = y[0, :] end_effector[0, :, 1] = y[0, :]
for i in range(1, self.n_links): for i in range(1, self.n_links):
endeffector[i, :, 0] = x[i, :] + endeffector[i - 1, -1, 0] end_effector[i, :, 0] = x[i, :] + end_effector[i - 1, -1, 0]
endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1] 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 # 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 # check if any of those points are below surface
nr_line_points_below_surface_before_hole = np.sum(line_points[r, c, 1] < 0) nr_line_points_below_surface_before_hole = np.sum(line_points[r, c, 1] < 0)
@ -192,7 +200,7 @@ class HoleReacher(gym.Env):
return True return True
# all points that are after the hole in x # 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 # check if any of those points are below surface
nr_line_points_below_surface_after_hole = np.sum(line_points[r, c, 1] < 0) nr_line_points_below_surface_after_hole = np.sum(line_points[r, c, 1] < 0)
@ -201,11 +209,11 @@ class HoleReacher(gym.Env):
return True return True
# all points that are above the hole # all points that are above the hole
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)) & (
line_points[:, :, 0] < (self.hole_x + self.hole_width / 2))) line_points[:, :, 0] < (self._tmp_hole_x + self._tmp_hole_width / 2)))
# check if any of those points are below surface # 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: if nr_line_points_below_surface_in_hole > 0:
return True return True
@ -214,64 +222,85 @@ class HoleReacher(gym.Env):
def render(self, mode='human'): def render(self, mode='human'):
if self.fig is None: if self.fig is None:
# Create base figure once on the beginning. Afterwards only update
plt.ion()
self.fig = plt.figure() self.fig = plt.figure()
# plt.ion() ax = self.fig.add_subplot(1, 1, 1)
# plt.pause(0.01)
else: # limits
plt.figure(self.fig.number) 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": if mode == "human":
plt.cla()
plt.title(f"Iteration: {self._steps}, distance: {self.end_effector - self.bottom_center_of_hole}")
# Arm # arm
plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') self.line.set_data(self._joints[:, 0], self._joints[:, 1])
# Add the patch to the Axes self.fig.canvas.draw()
[plt.gca().add_patch(rect) for rect in self.patches] self.fig.canvas.flush_events()
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()
elif mode == "partial": elif mode == "partial":
if self._steps == 1:
# fig, ax = plt.subplots()
# Add the patch to the Axes
try:
[plt.gca().add_patch(rect) for rect in self.patches]
except RuntimeError:
pass
# plt.pause(0.01)
if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided: if self._steps % 20 == 0 or self._steps in [1, 199] or self._is_collided:
# Arm # Arm
plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k', alpha=self._steps / 200) plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k',
# ax.plot(line_points_in_taskspace[:, 0, 0], alpha=self._steps / 200)
# 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)
lim = np.sum(self.link_lengths) + 0.5 def _set_patches(self):
plt.xlim([-lim, lim]) if self.fig is not None:
plt.ylim([-1.1, lim]) self.fig.gca().patches = []
plt.pause(0.01) 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": # Add the patch to the Axes
if self._steps == 199 or self._is_collided: self.fig.gca().add_patch(left_block)
# fig, ax = plt.subplots() self.fig.gca().add_patch(right_block)
self.fig.gca().add_patch(hole_floor)
# Add the patch to the Axes @property
[plt.gca().add_patch(rect) for rect in self.patches] 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) @property
# Arm def start_pos(self) -> Union[float, int, np.ndarray]:
plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') 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): def close(self):
if self.fig is not None: if self.fig is not None:
@ -281,22 +310,20 @@ class HoleReacher(gym.Env):
if __name__ == '__main__': if __name__ == '__main__':
nl = 5 nl = 5
render_mode = "human" # "human" or "partial" or "final" 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, env = HoleReacherEnv(n_links=nl, allow_self_collision=False, allow_wall_collision=False, hole_width=None,
hole_depth=1, hole_x=1) hole_depth=1, hole_x=None)
env.reset() obs = env.reset()
# env.render(mode=render_mode)
for i in range(200): for i in range(200):
# objective.load_result("/tmp/cma") # objective.load_result("/tmp/cma")
# test with random actions # test with random actions
ac = 2 * env.action_space.sample() ac = 2 * env.action_space.sample()
# ac[0] += np.pi/2
obs, rew, d, info = env.step(ac) obs, rew, d, info = env.step(ac)
env.render(mode=render_mode) env.render(mode=render_mode)
print(rew) print(rew)
if d: if d:
break env.reset()
env.close() env.close()

251
alr_envs/classic_control/simple_reacher.py
View File

@ -1,42 +1,41 @@
import gym from typing import Iterable, Union
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from gym import spaces from gym import spaces
from gym.utils import seeding 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): class SimpleReacherEnv(MPEnv):
# mpl.use('Qt5Agg')
class SimpleReacherEnv(gym.Env):
""" """
Simple Reaching Task without any physics simulation. 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 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. towards the end of the trajectory.
""" """
def __init__(self, n_links, random_start=True): def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True):
super().__init__() super().__init__()
self.link_lengths = np.ones(n_links) self.link_lengths = np.ones(n_links)
self.n_links = n_links self.n_links = n_links
self.dt = 0.01 self.dt = 0.1
self.random_start = random_start self.random_start = random_start
self._goal = None
self._joints = None self._joints = None
self._joint_angle = None self._joint_angles = None
self._angle_velocity = None self._angle_velocity = None
self._start_pos = 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 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([ state_bound = np.hstack([
[np.pi] * self.n_links, # cos [np.pi] * self.n_links, # cos
[np.pi] * self.n_links, # sin [np.pi] * self.n_links, # sin
@ -47,45 +46,76 @@ class SimpleReacherEnv(gym.Env):
self.action_space = spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape) 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.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.metadata = {'render.modes': ["human"]}
self.fig = None
self._steps = 0 self._steps = 0
self.seed() self.seed()
def step(self, action: np.ndarray): def step(self, action: np.ndarray):
"""
A single step with action in torque space
"""
# action = self._add_action_noise(action) # 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)
vel = action
# self._angle_velocity = self._angle_velocity + self.dt * action self._angle_velocity = self._angle_velocity + self.dt * ac
# self._joint_angle = angle_normalize(self._joint_angle + self.dt * self._angle_velocity) self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
self._angle_velocity = vel
self._joint_angle = self._joint_angle + self.dt * self._angle_velocity
self._update_joints() self._update_joints()
self._steps += 1
reward, info = self._get_reward(action) reward, info = self._get_reward(action)
# done = np.abs(self.end_effector - self._goal_pos) < 0.1 self._steps += 1
done = False done = False
return self._get_obs().copy(), reward, done, info return self._get_obs().copy(), reward, done, info
def _add_action_noise(self, action: np.ndarray): def reset(self):
"""
add unobserved Gaussian Noise N(0,0.01) to the actions
Args:
action:
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
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 _update_joints(self):
"""
update joints to get new end-effector position. The other links are only required for rendering.
Returns:
""" """
return self.np_random.normal(0, 0.1, *action.shape) + action 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
reward_dist = 0
if self._steps >= self.steps_before_reward:
reward_dist -= np.linalg.norm(diff)
# reward_dist = np.exp(-0.1 * diff ** 2).mean()
# reward_dist = - (diff ** 2).mean()
reward_ctrl = (action ** 2).sum()
reward = reward_dist - reward_ctrl
return reward, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
def _get_obs(self): def _get_obs(self):
theta = self._joint_angle theta = self._joint_angles
return np.hstack([ return np.hstack([
np.cos(theta), np.cos(theta),
np.sin(theta), np.sin(theta),
@ -94,91 +124,108 @@ class SimpleReacherEnv(gym.Env):
self._steps self._steps
]) ])
def _update_joints(self): def _generate_goal(self):
"""
update joints to get new end-effector position. The other links are only required for rendering.
Returns:
""" if self._target is None:
angles = np.cumsum(self._joint_angle) # center = self._joints[0]
x = self.link_lengths * np.vstack([np.cos(angles), np.sin(angles)]) # # Sample uniformly in circle with radius R around center of reacher.
self._joints[1:] = self._joints[0] + np.cumsum(x.T, axis=0) # R = np.sum(self.link_lengths)
# r = R * np.sqrt(self.np_random.uniform())
# theta = self.np_random.uniform() * 2 * np.pi
# goal = center + r * np.stack([np.cos(theta), np.sin(theta)])
def _get_reward(self, action: np.ndarray): total_length = np.sum(self.link_lengths)
diff = self.end_effector - self._goal goal = np.array([total_length, total_length])
reward_dist = 0 while np.linalg.norm(goal) >= total_length:
goal = self.np_random.uniform(low=-total_length, high=total_length, size=2)
# 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()
# reward_dist = - (diff ** 2).mean()
reward_ctrl = 1e-5 * (action ** 2).sum()
reward = reward_dist - reward_ctrl
return reward, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
def reset(self):
# 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_angle = np.hstack([[self.np_random.uniform(-np.pi, np.pi)], np.zeros(self.n_links - 1)])
else: else:
self._joint_angle = np.zeros(self.n_links) goal = np.copy(self._target)
self._start_pos = self._joint_angle self._goal = goal
self._angle_velocity = np.zeros(self.n_links)
self._joints = np.zeros((self.n_links + 1, 2))
self._update_joints()
self._steps = 0
self._goal = self._get_random_goal() def render(self, mode='human'): # pragma: no cover
return self._get_obs().copy() 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)
def _get_random_goal(self): # limits
center = self._joints[0] lim = np.sum(self.link_lengths) + 0.5
ax.set_xlim([-lim, lim])
ax.set_ylim([-lim, lim])
# Sample uniformly in circle with radius R around center of reacher. self.line, = ax.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
R = np.sum(self.link_lengths) goal_pos = self._goal.T
r = R * np.sqrt(self.np_random.uniform()) self.goal_point, = ax.plot(goal_pos[0], goal_pos[1], 'gx')
theta = np.pi/2 + 0.001 * np.random.randn() # self.np_random.uniform() * 2 * np.pi self.goal_dist, = ax.plot([self.end_effector[0], goal_pos[0]], [self.end_effector[1], goal_pos[1]], 'g--')
return center + r * np.stack([np.cos(theta), np.sin(theta)])
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): def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed) self.np_random, seed = seeding.np_random(seed)
return [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}")
# Arm
plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
# goal
goal_pos = self._goal.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): def close(self):
del self.fig del self.fig
@property @property
def end_effector(self): def end_effector(self):
return self._joints[self.n_links].T 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()

206
alr_envs/classic_control/viapoint_reacher.py
View File

@ -1,19 +1,31 @@
from typing import Iterable, Union
import gym import gym
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import numpy as np import numpy as np
from gym.utils import seeding
from alr_envs.classic_control.utils import check_self_collision 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): def __init__(self, n_links, random_start: bool = True, via_target: Union[None, Iterable] = None,
self.num_links = n_links 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)) self.link_lengths = np.ones((n_links, 1))
# task self.random_start = random_start
self.via_point = np.ones(2)
self.goal_point = np.array((n_links, 0)) # 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 # collision
self.allow_self_collision = allow_self_collision self.allow_self_collision = allow_self_collision
@ -23,78 +35,74 @@ class ViaPointReacher(gym.Env):
self._joints = None self._joints = None
self._joint_angles = None self._joint_angles = None
self._angle_velocity = None self._angle_velocity = None
self.start_pos = np.hstack([[np.pi / 2], np.zeros(self.num_links - 1)]) self._start_pos = np.hstack([[np.pi / 2], np.zeros(self.n_links - 1)])
self.start_vel = np.zeros(self.num_links) self._start_vel = np.zeros(self.n_links)
self.weight_matrix_scale = 1 self.weight_matrix_scale = 1
self._steps = 0
self.dt = 0.01 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([ state_bound = np.hstack([
[np.pi] * self.num_links, # cos [np.pi] * self.n_links, # cos
[np.pi] * self.num_links, # sin [np.pi] * self.n_links, # sin
[np.inf] * self.num_links, # velocity [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] * 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.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) 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 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 self._steps = 0
self.seed()
return self._get_obs().copy()
def step(self, action: np.ndarray): def step(self, action: np.ndarray):
""" """
a single step with an action in joint velocity space a single step with an action in joint velocity space
""" """
vel = action vel = action
acc = (vel - self._angle_velocity) / self.dt
self._angle_velocity = vel self._angle_velocity = vel
self._joint_angles = self._joint_angles + self.dt * self._angle_velocity self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
self._update_joints() self._update_joints()
dist_reward = 0 acc = (vel - self._angle_velocity) / self.dt
if not self._is_collided: reward, info = self._get_reward(acc)
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)
# TODO: Do we need that? info.update({"is_collided": self._is_collided})
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}
self._steps += 1 self._steps += 1
# done = self._steps * self.dt > self.time_limit or self._is_collided
done = self._is_collided done = self._is_collided
return self._get_obs().copy(), reward, done, info 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):
self._via_point = self.np_random.uniform(0.5, 3.5, 2) if self._via_target is None else np.copy(self._via_target)
self._goal = self.np_random.uniform(0.5, 0.1, 2) if self._target is None else np.copy(self._target)
# raise NotImplementedError("How to properly sample points??")
def _update_joints(self): def _update_joints(self):
""" """
update _joints to get new end effector position. The other links are only required for rendering. update _joints to get new end effector position. The other links are only required for rendering.
@ -115,14 +123,38 @@ class ViaPointReacher(gym.Env):
self._is_collided = self_collision 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): def _get_obs(self):
theta = self._joint_angles theta = self._joint_angles
return np.hstack([ return np.hstack([
np.cos(theta), np.cos(theta),
np.sin(theta), np.sin(theta),
self._angle_velocity, self._angle_velocity,
self.end_effector - self.via_point, self.end_effector - self._via_point,
self.end_effector - self.goal_point, self.end_effector - self._goal,
self._steps self._steps
]) ])
@ -133,7 +165,7 @@ class ViaPointReacher(gym.Env):
accumulated_theta = np.cumsum(theta, axis=0) 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 x = np.cos(accumulated_theta) * self.link_lengths * intermediate_points
y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points y = np.sin(accumulated_theta) * self.link_lengths * intermediate_points
@ -141,33 +173,46 @@ class ViaPointReacher(gym.Env):
endeffector[0, :, 0] = x[0, :] endeffector[0, :, 0] = x[0, :]
endeffector[0, :, 1] = y[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, :, 0] = x[i, :] + endeffector[i - 1, -1, 0]
endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1] endeffector[i, :, 1] = y[i, :] + endeffector[i - 1, -1, 1]
return np.squeeze(endeffector + self._joints[0, :]) return np.squeeze(endeffector + self._joints[0, :])
def render(self, mode='human'): def render(self, mode='human'):
goal_pos = self._goal.T
via_pos = self._via_point.T
if self.fig is None: if self.fig is None:
# Create base figure once on the beginning. Afterwards only update
plt.ion()
self.fig = plt.figure() self.fig = plt.figure()
# plt.ion() ax = self.fig.add_subplot(1, 1, 1)
# plt.pause(0.01)
else: # limits
plt.figure(self.fig.number) 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": if mode == "human":
plt.cla() # goal
plt.title(f"Iteration: {self._steps}") 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 # arm
plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') self.line.set_data(self._joints[:, 0], self._joints[:, 1])
lim = np.sum(self.link_lengths) + 0.5 self.fig.canvas.draw()
plt.xlim([-lim, lim]) self.fig.canvas.flush_events()
plt.ylim([-lim, lim])
# plt.draw()
plt.pause(1e-4) # pushes window to foreground, which is annoying.
# self.fig.canvas.flush_events()
elif mode == "partial": elif mode == "partial":
if self._steps == 1: if self._steps == 1:
@ -196,12 +241,39 @@ class ViaPointReacher(gym.Env):
# Add the patch to the Axes # Add the patch to the Axes
[plt.gca().add_patch(rect) for rect in self.patches] [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 # Arm
plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k') plt.plot(self._joints[:, 0], self._joints[:, 1], 'ro-', markerfacecolor='k')
plt.pause(0.01) 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): def close(self):
if self.fig is not None: if self.fig is not None:
plt.close(self.fig) plt.close(self.fig)

50
alr_envs/mujoco/ball_in_a_cup/utils.py
View File

@ -1,5 +1,5 @@
from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper
from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper from alr_envs.utils.mps.dmp_wrapper import DmpWrapper
from alr_envs.mujoco.ball_in_a_cup.ball_in_a_cup import ALRBallInACupEnv 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(): def _init():
env = ALRBallInACupEnv(reward_type="contextual_goal") env = ALRBallInACupEnv(reward_type="contextual_goal")
env = DetPMPWrapper(env, env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
num_dof=7, policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True)
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.seed(seed + rank) env.seed(seed + rank)
return env return env
@ -51,19 +40,8 @@ def make_env(rank, seed=0):
def _init(): def _init():
env = ALRBallInACupEnv(reward_type="simple") env = ALRBallInACupEnv(reward_type="simple")
env = DetPMPWrapper(env, env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
num_dof=7, policy_type="motor", weights_scale=0.2, zero_start=True, zero_goal=True)
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.seed(seed + rank) env.seed(seed + rank)
return env return env
@ -85,20 +63,8 @@ def make_simple_env(rank, seed=0):
def _init(): def _init():
env = ALRBallInACupEnv(reward_type="simple") env = ALRBallInACupEnv(reward_type="simple")
env = DetPMPWrapper(env, env = DetPMPWrapper(env, num_dof=3, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
num_dof=3, policy_type="motor", weights_scale=0.25, zero_start=True, zero_goal=True, off=-0.1)
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.seed(seed + rank) env.seed(seed + rank)
return env return env

47
alr_envs/mujoco/beerpong/utils.py
View File

@ -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 import ALRBeerpongEnv
from alr_envs.mujoco.beerpong.beerpong_simple import ALRBeerpongEnv as ALRBeerpongEnvSimple from alr_envs.mujoco.beerpong.beerpong_simple import ALRBeerpongEnv as ALRBeerpongEnvSimple
@ -17,19 +17,8 @@ def make_contextual_env(rank, seed=0):
def _init(): def _init():
env = ALRBeerpongEnv() env = ALRBeerpongEnv()
env = DetPMPWrapper(env, env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
num_dof=7, policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True)
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.seed(seed + rank) env.seed(seed + rank)
return env return env
@ -51,19 +40,8 @@ def make_env(rank, seed=0):
def _init(): def _init():
env = ALRBeerpongEnvSimple() env = ALRBeerpongEnvSimple()
env = DetPMPWrapper(env, env = DetPMPWrapper(env, num_dof=7, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
num_dof=7, policy_type="motor", weights_scale=0.25, zero_start=True, zero_goal=True)
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.seed(seed + rank) env.seed(seed + rank)
return env return env
@ -85,19 +63,8 @@ def make_simple_env(rank, seed=0):
def _init(): def _init():
env = ALRBeerpongEnvSimple() env = ALRBeerpongEnvSimple()
env = DetPMPWrapper(env, env = DetPMPWrapper(env, num_dof=3, num_basis=5, width=0.005, duration=3.5, dt=env.dt, post_traj_time=4.5,
num_dof=3, policy_type="motor", weights_scale=0.5, zero_start=True, zero_goal=True)
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.seed(seed + rank) env.seed(seed + rank)
return env return env

65
alr_envs/utils/legacy/utils.py
View File

@ -1,7 +1,7 @@
import alr_envs.classic_control.hole_reacher as hr import alr_envs.classic_control.hole_reacher as hr
import alr_envs.classic_control.viapoint_reacher as vpr import alr_envs.classic_control.viapoint_reacher as vpr
from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper from alr_envs.utils.mps.dmp_wrapper import DmpWrapper
from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper
import numpy as np import numpy as np
@ -49,13 +49,13 @@ def make_holereacher_env(rank, seed=0):
""" """
def _init(): def _init():
_env = hr.HoleReacher(n_links=5, _env = hr.HoleReacherEnv(n_links=5,
allow_self_collision=False, allow_self_collision=False,
allow_wall_collision=False, allow_wall_collision=False,
hole_width=0.25, hole_width=0.25,
hole_depth=1, hole_depth=1,
hole_x=2, hole_x=2,
collision_penalty=100) collision_penalty=100)
_env = DmpWrapper(_env, _env = DmpWrapper(_env,
num_dof=5, num_dof=5,
@ -65,7 +65,7 @@ def make_holereacher_env(rank, seed=0):
dt=_env.dt, dt=_env.dt,
learn_goal=True, learn_goal=True,
alpha_phase=2, alpha_phase=2,
start_pos=_env.start_pos, start_pos=_env._start_pos,
policy_type="velocity", policy_type="velocity",
weights_scale=50, weights_scale=50,
goal_scale=0.1 goal_scale=0.1
@ -89,13 +89,13 @@ def make_holereacher_fix_goal_env(rank, seed=0):
""" """
def _init(): def _init():
_env = hr.HoleReacher(n_links=5, _env = hr.HoleReacherEnv(n_links=5,
allow_self_collision=False, allow_self_collision=False,
allow_wall_collision=False, allow_wall_collision=False,
hole_width=0.15, hole_width=0.15,
hole_depth=1, hole_depth=1,
hole_x=1, hole_x=1,
collision_penalty=100) collision_penalty=100)
_env = DmpWrapper(_env, _env = DmpWrapper(_env,
num_dof=5, num_dof=5,
@ -105,7 +105,7 @@ def make_holereacher_fix_goal_env(rank, seed=0):
learn_goal=False, learn_goal=False,
final_pos=np.array([2.02669572, -1.25966385, -1.51618198, -0.80946476, 0.02012344]), final_pos=np.array([2.02669572, -1.25966385, -1.51618198, -0.80946476, 0.02012344]),
alpha_phase=2, alpha_phase=2,
start_pos=_env.start_pos, start_pos=_env._start_pos,
policy_type="velocity", policy_type="velocity",
weights_scale=50, weights_scale=50,
goal_scale=1 goal_scale=1
@ -129,27 +129,16 @@ def make_holereacher_env_pmp(rank, seed=0):
""" """
def _init(): def _init():
_env = hr.HoleReacher(n_links=5, _env = hr.HoleReacherEnv(n_links=5,
allow_self_collision=False, allow_self_collision=False,
allow_wall_collision=False, allow_wall_collision=False,
hole_width=0.15, hole_width=0.15,
hole_depth=1, hole_depth=1,
hole_x=1, hole_x=1,
collision_penalty=1000) collision_penalty=1000)
_env = DetPMPWrapper(_env, _env = DetPMPWrapper(_env, num_dof=5, num_basis=5, width=0.02, duration=2, dt=_env.dt, post_traj_time=0,
num_dof=5, policy_type="velocity", weights_scale=0.2, zero_start=True, zero_goal=False)
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) _env.seed(seed + rank)
return _env return _env

4
alr_envs/utils/make_env_helpers.py
View File

@ -1,5 +1,5 @@
from alr_envs.utils.wrapper.dmp_wrapper import DmpWrapper from alr_envs.utils.mps.dmp_wrapper import DmpWrapper
from alr_envs.utils.wrapper.detpmp_wrapper import DetPMPWrapper from alr_envs.utils.mps.detpmp_wrapper import DetPMPWrapper
import gym import gym
from gym.vector.utils import write_to_shared_memory from gym.vector.utils import write_to_shared_memory
import sys import sys

0
alr_envs/utils/wrapper/__init__.py → alr_envs/utils/mps/__init__.py
View File

21
alr_envs/utils/wrapper/detpmp_wrapper.py → alr_envs/utils/mps/detpmp_wrapper.py
View File

@ -2,26 +2,27 @@ import gym
import numpy as np import numpy as np
from mp_lib import det_promp 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): class DetPMPWrapper(MPWrapper):
def __init__(self, env, num_dof, num_basis, width, start_pos=None, duration=1, dt=0.01, post_traj_time=0., def __init__(self, env: MPEnv, num_dof: int, num_basis: int, width: int, duration: int = 1, dt: float = 0.01,
policy_type=None, weights_scale=1, zero_start=False, zero_goal=False, **mp_kwargs): post_traj_time: float = 0., policy_type: str = None, weights_scale: float = 1.,
# self.duration = duration # seconds 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, super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, num_basis=num_basis,
num_basis=num_basis, width=width, start_pos=start_pos, zero_start=zero_start, width=width, zero_start=zero_start, zero_goal=zero_goal, **mp_kwargs)
zero_goal=zero_goal)
self.dt = dt
action_bounds = np.inf * np.ones((self.mp.n_basis * self.mp.n_dof)) 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.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, 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, 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) zero_start=zero_start, zero_goal=zero_goal)

58
alr_envs/utils/wrapper/dmp_wrapper.py → alr_envs/utils/mps/dmp_wrapper.py
View File

@ -1,19 +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 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): class DmpWrapper(MPWrapper):
def __init__(self, env: gym.Env, num_dof: int, num_basis: int, def __init__(self, env: MPEnv, 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, duration: int = 1, alpha_phase: float = 2., dt: float = None,
learn_goal: bool = False, return_to_start: bool = False, post_traj_time: float = 0., learn_goal: bool = False, post_traj_time: float = 0.,
weights_scale: float = 1., goal_scale: float = 1., bandwidth_factor: float = 3., weights_scale: float = 1., goal_scale: float = 1., bandwidth_factor: float = 3.,
policy_type: str = None, render_mode: str = None): policy_type: str = None, render_mode: str = None):
@ -23,8 +22,6 @@ class DmpWrapper(MPWrapper):
env: env:
num_dof: num_dof:
num_basis: num_basis:
start_pos:
final_pos:
duration: duration:
alpha_phase: alpha_phase:
dt: dt:
@ -37,30 +34,17 @@ class DmpWrapper(MPWrapper):
self.learn_goal = learn_goal self.learn_goal = learn_goal
dt = env.dt if hasattr(env, "dt") else dt dt = env.dt if hasattr(env, "dt") else dt
assert dt is not None 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
# TODO: assert start_pos is not None # start_pos will be set in initialize, do we need this here?
# if learn_goal:
# final_pos = np.zeros_like(start_pos) # arbitrary, will be learned
# final_pos = np.zeros((1, num_dof)) # 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.t = np.linspace(0, duration, int(duration / dt))
self.goal_scale = goal_scale self.goal_scale = goal_scale
super().__init__(env, num_dof, duration, dt, post_traj_time, policy_type, weights_scale, render_mode, super().__init__(env, num_dof, dt, duration, post_traj_time, policy_type, weights_scale, render_mode,
num_basis=num_basis, num_basis=num_basis, alpha_phase=alpha_phase, bandwidth_factor=bandwidth_factor)
# start_pos=start_pos, final_pos=final_pos,
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))) 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) 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, def initialize_mp(self, num_dof: int, duration: int, dt: float, num_basis: int = 5, alpha_phase: float = 2.,
# start_pos: np.ndarray = None, bandwidth_factor: int = 3):
# final_pos: np.ndarray = None,
alpha_phase: float = 2., bandwidth_factor: float = 3.):
phase_generator = ExpDecayPhaseGenerator(alpha_phase=alpha_phase, duration=duration) phase_generator = ExpDecayPhaseGenerator(alpha_phase=alpha_phase, duration=duration)
basis_generator = DMPBasisGenerator(phase_generator, duration=duration, num_basis=num_basis, basis_generator = DMPBasisGenerator(phase_generator, duration=duration, num_basis=num_basis,
@ -69,15 +53,6 @@ class DmpWrapper(MPWrapper):
dmp = dmps.DMP(num_dof=num_dof, basis_generator=basis_generator, phase_generator=phase_generator, dmp = dmps.DMP(num_dof=num_dof, basis_generator=basis_generator, phase_generator=phase_generator,
num_time_steps=int(duration / dt), dt=dt) num_time_steps=int(duration / dt), dt=dt)
# dmp.dmp_start_pos = start_pos.reshape((1, num_dof))
# in a contextual environment, the start_pos may be not fixed, set in mp_rollout?
# TODO: Should we set start_pos in init at all? It's only used after calling rollout anyway...
# dmp.dmp_start_pos = start_pos.reshape((1, num_dof)) if start_pos is not None else np.zeros((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 return dmp
def goal_and_weights(self, params): def goal_and_weights(self, params):
@ -87,18 +62,15 @@ class DmpWrapper(MPWrapper):
if self.learn_goal: if self.learn_goal:
goal_pos = params[0, -self.mp.num_dimensions:] # [num_dof] goal_pos = params[0, -self.mp.num_dimensions:] # [num_dof]
params = params[:, :-self.mp.num_dimensions] # [1,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: else:
goal_pos = self.env.goal_pos # self.mp.dmp_goal_pos.flatten() goal_pos = self.env.goal_pos
assert goal_pos is not None 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 return goal_pos * self.goal_scale, weight_matrix * self.weights_scale
def mp_rollout(self, action): def mp_rollout(self, action):
# if self.mp.start_pos is None: self.mp.dmp_start_pos = self.env.start_pos
self.mp.dmp_start_pos = self.env.init_qpos.reshape((1, self.num_dof)) # start_pos
goal_pos, weight_matrix = self.goal_and_weights(action) goal_pos, weight_matrix = self.goal_and_weights(action)
self.mp.set_weights(weight_matrix, goal_pos) self.mp.set_weights(weight_matrix, goal_pos)
return self.mp.reference_trajectory(self.t) return self.mp.reference_trajectory(self.t)

33
alr_envs/utils/mps/mp_environments.py Normal file
View File

@ -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 value for each observation entry
whether the observation is returned by the DMP for the contextual case or not.
This effectively allows to filter unwanted or unnecessary observations from the full step-based case.
"""
raise NotImplementedError()
@property
@abstractmethod
def start_pos(self) -> Union[float, int, np.ndarray]:
"""
Returns the current 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

57
alr_envs/utils/wrapper/mp_wrapper.py → alr_envs/utils/mps/mp_wrapper.py
View File

@ -1,32 +1,24 @@
from abc import ABC, abstractmethod from abc import ABC, abstractmethod
from collections import defaultdict
import gym import gym
import numpy as np import numpy as np
from alr_envs.utils.mps.mp_environments import MPEnv
from alr_envs.utils.policies import get_policy_class from alr_envs.utils.policies import get_policy_class
class MPWrapper(gym.Wrapper, ABC): class MPWrapper(gym.Wrapper, ABC):
def __init__(self, def __init__(self, env: MPEnv, num_dof: int, dt: float, duration: int = 1, post_traj_time: float = 0.,
env: gym.Env, policy_type: str = None, weights_scale: float = 1., render_mode: str = None, **mp_kwargs):
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
):
super().__init__(env) super().__init__(env)
self.num_dof = num_dof # adjust observation space to reduce version
# self.num_basis = num_basis obs_sp = self.env.observation_space
# self.duration = duration # seconds 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 assert dt is not None # this should never happen as MPWrapper is a base class
self.post_traj_steps = int(post_traj_time / dt) 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_mode = render_mode
self.render_kwargs = {} 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): def __call__(self, params, contexts=None):
"""
Can be used to provide a batch of parameter sets
"""
params = np.atleast_2d(params) params = np.atleast_2d(params)
obs = [] obs = []
rewards = [] rewards = []
@ -50,7 +45,6 @@ class MPWrapper(gym.Wrapper, ABC):
# for p, c in zip(params, contexts): # for p, c in zip(params, contexts):
for p in params: for p in params:
# self.configure(c) # self.configure(c)
# context = self.reset()
ob, reward, done, info = self.step(p) ob, reward, done, info = self.step(p)
obs.append(ob) obs.append(ob)
rewards.append(reward) rewards.append(reward)
@ -63,8 +57,7 @@ class MPWrapper(gym.Wrapper, ABC):
self.env.configure(context) self.env.configure(context)
def reset(self): def reset(self):
obs = self.env.reset() return self.env.reset()[self.env.active_obs]
return obs
def step(self, action: np.ndarray): 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""" """ This function generates a trajectory based on a DMP and then does the usual loop over reset and step"""
@ -78,15 +71,9 @@ class MPWrapper(gym.Wrapper, ABC):
# self._velocity = velocity # self._velocity = velocity
rewards = 0 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
# TODO: Explicitly call reset before step to obtain context from obs?
# self.env.configure(context)
# obs = self.env.reset()
info = {} 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)): for t, pos_vel in enumerate(zip(trajectory, velocity)):
ac = self.policy.get_action(pos_vel[0], pos_vel[1]) ac = self.policy.get_action(pos_vel[0], pos_vel[1])
@ -100,7 +87,7 @@ class MPWrapper(gym.Wrapper, ABC):
break break
done = True done = True
return obs, rewards, done, info return obs[self.env.active_obs], rewards, done, info
def render(self, mode='human', **kwargs): def render(self, mode='human', **kwargs):
"""Only set render options here, such that they can be used during the rollout. """Only set render options here, such that they can be used during the rollout.
@ -108,18 +95,6 @@ class MPWrapper(gym.Wrapper, ABC):
self.render_mode = mode self.render_mode = mode
self.render_kwargs = kwargs 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 @abstractmethod
def mp_rollout(self, action): def mp_rollout(self, action):
""" """

11
example.py
View File

@ -46,7 +46,7 @@ def example_dmp():
obs = env.reset() 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): def make_env(env_id, seed, rank):
env = gym.make(env_id) env = gym.make(env_id)
env.seed(seed + rank) env.seed(seed + rank)
@ -73,7 +73,7 @@ def example_async(n_cpu=4, seed=int('533D', 16)):
# do not return values above threshold # do not return values above threshold
return (*map(lambda v: np.stack(v)[:n_samples], vals.values()),) 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() obs = envs.reset()
print(sample(envs, 16)) print(sample(envs, 16))
@ -82,7 +82,6 @@ def example_async(n_cpu=4, seed=int('533D', 16)):
if __name__ == '__main__': if __name__ == '__main__':
# example_mujoco() # example_mujoco()
# example_dmp() # example_dmp()
# example_async() example_async("alr_envs:LongSimpleReacherDMP-v0", 4)
env = gym.make("alr_envs:HoleReacherDMP-v0") # env = gym.make("alr_envs:HoleReacherDMP-v0", context=0.1)
# env = gym.make("alr_envs:SimpleReacherDMP-v1") # env = gym.make("alr_envs:HoleReacherDMP-v1")
print()