removed old holereacher env

alr_envs/alr/__init__.py

@@ -362,7 +362,7 @@ for _v in _versions:
                 "num_basis": 5,
                 "duration": 2,
                 "learn_goal": True,
-                "alpha_phase": 2,
+                "alpha_phase": 2.5,
                 "bandwidth_factor": 2,
                 "policy_type": "velocity",
                 "weights_scale": 50,
@@ -384,7 +384,7 @@ for _v in _versions:
                 "num_basis": 5,
                 "duration": 2,
                 "policy_type": "velocity",
-                "weights_scale": 0.2,
+                "weights_scale": 0.1,
                 "zero_start": True
             }
         }

alr_envs/alr/classic_control/__init__.py

@@ -1,3 +1,3 @@
-from .hole_reacher.hole_reacher import HoleReacherEnv, HoleReacherEnvOld
+from .hole_reacher.hole_reacher import HoleReacherEnv
 from .simple_reacher.simple_reacher import SimpleReacherEnv
 from .viapoint_reacher.viapoint_reacher import ViaPointReacherEnv

alr_envs/alr/classic_control/hole_reacher/hole_reacher.py

@@ -42,6 +42,9 @@ class HoleReacherEnv(BaseReacherDirectEnv):
         if rew_fct == "simple":
             from alr_envs.alr.classic_control.hole_reacher.hr_simple_reward import HolereacherReward
             self.reward_function = HolereacherReward(allow_self_collision, allow_wall_collision, collision_penalty)
+        elif rew_fct == "vel_acc":
+            from alr_envs.alr.classic_control.hole_reacher.hr_dist_vel_acc_reward import HolereacherReward
+            self.reward_function = HolereacherReward(allow_self_collision, allow_wall_collision, collision_penalty)
         else:
             raise ValueError("Unknown reward function {}".format(rew_fct))
 

alr_envs/alr/classic_control/hole_reacher/hole_reacher_old.py

@@ -1,315 +0,0 @@
-from typing import Union
-
-import gym
-import matplotlib.pyplot as plt
-import numpy as np
-from gym.utils import seeding
-from matplotlib import patches
-
-from alr_envs.alr.classic_control.base_reacher.base_reacher_direct import BaseReacherDirectEnv
-from alr_envs.alr.classic_control.utils import check_self_collision
-
-
-class HoleReacherEnvOld(gym.Env):
-
-    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: float = 1000):
-
-        self.n_links = n_links
-        self.link_lengths = np.ones((n_links, 1))
-
-        self.random_start = random_start
-
-        # provided initial parameters
-        self.initial_x = hole_x  # x-position of center of hole
-        self.initial_width = hole_width  # width of hole
-        self.initial_depth = hole_depth  # depth of hole
-
-        # temp container for current env state
-        self._tmp_x = None
-        self._tmp_width = None
-        self._tmp_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
-        self.allow_wall_collision = allow_wall_collision
-        self.collision_penalty = collision_penalty
-
-        # state
-        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._dt = 0.01
-
-        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
-
-        self._steps = 0
-        self.seed()
-
-    @property
-    def dt(self) -> Union[float, int]:
-        return self._dt
-
-    # @property
-    # def start_pos(self):
-    #     return self._start_pos
-
-    @property
-    def current_pos(self):
-        return self._joint_angles.copy()
-
-    @property
-    def current_vel(self):
-        return self._angle_velocity.copy()
-
-    def step(self, action: np.ndarray):
-        """
-        A single step with an action in joint velocity space
-        """
-
-        acc = (action - self._angle_velocity) / self.dt
-        self._angle_velocity = action
-        self._joint_angles = self._joint_angles + self.dt * self._angle_velocity  # + 0.001 * np.random.randn(5)
-        self._update_joints()
-
-        reward, info = self._get_reward(acc)
-
-        info.update({"is_collided": self._is_collided})
-        self.end_effector_traj.append(np.copy(self.end_effector))
-
-        self._steps += 1
-        done = self._is_collided
-
-        return self._get_obs().copy(), reward, done, info
-
-    def reset(self):
-        if self.random_start:
-            # Maybe change more than first 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
-        self.end_effector_traj = []
-
-        return self._get_obs().copy()
-
-    def _generate_hole(self):
-        if self.initial_width is None:
-            width = self.np_random.uniform(0.15, 0.5)
-        else:
-            width = np.copy(self.initial_width)
-        if self.initial_x is None:
-            # sample whole on left or right side
-            direction = self.np_random.choice([-1, 1])
-            # Hole center needs to be half the width away from the arm to give a valid setting.
-            x = direction * self.np_random.uniform(width / 2, 3.5)
-        else:
-            x = np.copy(self.initial_x)
-        if self.initial_depth is None:
-            # TODO we do not want this right now.
-            depth = self.np_random.uniform(1, 1)
-        else:
-            depth = np.copy(self.initial_depth)
-
-        self._tmp_width = width
-        self._tmp_x = x
-        self._tmp_depth = depth
-        self._goal = np.hstack([self._tmp_x, -self._tmp_depth])
-
-    def _update_joints(self):
-        """
-        update _joints to get new end effector position. The other links are only required for rendering.
-        Returns:
-
-        """
-        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]
-
-        self_collision = False
-        wall_collision = False
-
-        if not self.allow_self_collision:
-            self_collision = check_self_collision(line_points_in_taskspace)
-            if np.any(np.abs(self._joint_angles) > np.pi) and not self.allow_self_collision:
-                self_collision = True
-
-        if not self.allow_wall_collision:
-            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):
-        reward = 0
-        # success = False
-
-        if self._steps == 199 or self._is_collided:
-            # return reward only in last time step
-            # Episode also terminates when colliding, hence return reward
-            dist = np.linalg.norm(self.end_effector - self._goal)
-            # success = dist < 0.005 and not self._is_collided
-            reward = - dist ** 2 - self.collision_penalty * self._is_collided
-
-        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._tmp_width,
-            # self._tmp_hole_depth,
-            self.end_effector - self._goal,
-            self._steps
-        ])
-
-    def _get_forward_kinematics(self, num_points_per_link=1):
-        theta = self._joint_angles[:, None]
-
-        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)
-        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
-
-        end_effector[0, :, 0] = x[0, :]
-        end_effector[0, :, 1] = y[0, :]
-
-        for i in range(1, self.n_links):
-            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(end_effector + self._joints[0, :])
-
-    def _check_wall_collision(self, line_points):
-        # all points that are before the hole in x
-        r, c = np.where(line_points[:, :, 0] < (self._tmp_x - self._tmp_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)
-
-        if nr_line_points_below_surface_before_hole > 0:
-            return True
-
-        # all points that are after the hole in x
-        r, c = np.where(line_points[:, :, 0] > (self._tmp_x + self._tmp_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)
-
-        if nr_line_points_below_surface_after_hole > 0:
-            return True
-
-        # all points that are above the hole
-        r, c = np.where((line_points[:, :, 0] > (self._tmp_x - self._tmp_width / 2)) & (
-                line_points[:, :, 0] < (self._tmp_x + self._tmp_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._tmp_depth)
-
-        if nr_line_points_below_surface_in_hole > 0:
-            return True
-
-        return False
-
-    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()
-            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: {np.linalg.norm(self.end_effector - self._goal) ** 2}")
-
-        if mode == "human":
-
-            # arm
-            self.line.set_data(self._joints[:, 0], self._joints[:, 1])
-
-            self.fig.canvas.draw()
-            self.fig.canvas.flush_events()
-
-        elif mode == "partial":
-            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)
-
-    def _set_patches(self):
-        if self.fig is not None:
-            self.fig.gca().patches = []
-            left_block = patches.Rectangle((-self.n_links, -self._tmp_depth),
-                                           self.n_links + self._tmp_x - self._tmp_width / 2,
-                                           self._tmp_depth,
-                                           fill=True, edgecolor='k', facecolor='k')
-            right_block = patches.Rectangle((self._tmp_x + self._tmp_width / 2, -self._tmp_depth),
-                                            self.n_links - self._tmp_x + self._tmp_width / 2,
-                                            self._tmp_depth,
-                                            fill=True, edgecolor='k', facecolor='k')
-            hole_floor = patches.Rectangle((self._tmp_x - self._tmp_width / 2, -self._tmp_depth),
-                                           self._tmp_width,
-                                           1 - self._tmp_depth,
-                                           fill=True, edgecolor='k', facecolor='k')
-
-            # 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)
-
-    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):
-        super().close()
-        if self.fig is not None:
-            plt.close(self.fig)

alr_envs/alr/classic_control/hole_reacher/hr_dist_vel_acc_reward.py

@@ -11,10 +11,11 @@ class HolereacherReward:
         self.collision_penalty = collision_penalty
         self._is_collided = False
 
-        self.reward_factors = np.array((-1, -1e-4, -1e-6, -collision_penalty, -1))
+        self.reward_factors = np.array((-1, -1e-4, -1e-6, -collision_penalty, 0))
 
     def reset(self):
         self._is_collided = False
+        self.collision_dist = 0
 
     def get_reward(self, env):
         dist_cost = 0
@@ -25,6 +26,7 @@ class HolereacherReward:
         self_collision = False
         wall_collision = False
 
+        if not self._is_collided:
             if not self.allow_self_collision:
                 self_collision = env._check_self_collision()
 
@@ -33,14 +35,16 @@ class HolereacherReward:
 
             self._is_collided = self_collision or wall_collision
 
-        if env._steps == 199 or self._is_collided:
+            self.collision_dist = np.linalg.norm(env.end_effector - env._goal)
+
+        if env._steps == 199: #  or self._is_collided:
             # return reward only in last time step
             # Episode also terminates when colliding, hence return reward
             dist = np.linalg.norm(env.end_effector - env._goal)
 
             success = dist < 0.005 and not self._is_collided
-            dist_cost = int(not self._is_collided) * dist ** 2
+            dist_cost = dist ** 2
-            collision_cost = self._is_collided * dist ** 2
+            collision_cost = self._is_collided * self.collision_dist ** 2
             time_cost = 199 - env._steps
 
         info = {"is_success": success,