reacher envs cleanup
- base classes for direct and torque control - added promp wrapper support
This commit is contained in:
Maximilian Huettenrauch
@@ -1,26 +1,22 @@
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from typing import Iterable, Union
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import gym
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import matplotlib.pyplot as plt
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import numpy as np
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from gym import spaces
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from gym.utils import seeding
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from alr_envs.alr.classic_control.base_reacher.base_reacher_torque import BaseReacherTorqueEnv
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class SimpleReacherEnv(gym.Env):
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class SimpleReacherEnv(BaseReacherTorqueEnv):
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"""
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Simple Reaching Task without any physics simulation.
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Returns no reward until 150 time steps. This allows the agent to explore the space, but requires precise actions
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towards the end of the trajectory.
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"""
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def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True):
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super().__init__()
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self.link_lengths = np.ones(n_links)
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self.n_links = n_links
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self._dt = 0.1
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self.random_start = random_start
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def __init__(self, n_links: int, target: Union[None, Iterable] = None, random_start: bool = True,
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allow_self_collision: bool = False,):
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super().__init__(n_links, random_start, allow_self_collision)
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# provided initial parameters
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self.inital_target = target
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@@ -28,16 +24,10 @@ class SimpleReacherEnv(gym.Env):
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# temp container for current env state
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self._goal = None
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self._joints = None
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self._joint_angles = None
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self._angle_velocity = None
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self._start_pos = np.zeros(self.n_links)
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self._start_vel = np.zeros(self.n_links)
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self.max_torque = 1
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self.steps_before_reward = 199
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action_bound = np.ones((self.n_links,)) * self.max_torque
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state_bound = np.hstack([
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[np.pi] * self.n_links, # cos
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[np.pi] * self.n_links, # sin
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@@ -45,84 +35,24 @@ class SimpleReacherEnv(gym.Env):
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[np.inf] * 2, # x-y coordinates of target distance
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[np.inf] # env steps, because reward start after n steps TODO: Maybe
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])
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self.action_space = spaces.Box(low=-action_bound, high=action_bound, shape=action_bound.shape)
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self.observation_space = spaces.Box(low=-state_bound, high=state_bound, shape=state_bound.shape)
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# containers for plotting
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self.metadata = {'render.modes': ["human"]}
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self.fig = None
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self._steps = 0
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self.seed()
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@property
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def dt(self) -> Union[float, int]:
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return self._dt
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# @property
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# def start_pos(self):
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# return self._start_pos
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@property
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def current_pos(self):
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return self._joint_angles
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@property
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def current_vel(self):
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return self._angle_velocity
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def step(self, action: np.ndarray):
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"""
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A single step with action in torque space
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"""
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# action = self._add_action_noise(action)
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ac = np.clip(action, -self.max_torque, self.max_torque)
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self._angle_velocity = self._angle_velocity + self.dt * ac
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self._joint_angles = self._joint_angles + self.dt * self._angle_velocity
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self._update_joints()
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reward, info = self._get_reward(action)
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self._steps += 1
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done = False
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return self._get_obs().copy(), reward, done, info
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def reset(self):
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# TODO: maybe do initialisation more random?
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# Sample only orientation of first link, i.e. the arm is always straight.
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if self.random_start:
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self._joint_angles = np.hstack([[self.np_random.uniform(-np.pi, np.pi)], np.zeros(self.n_links - 1)])
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self._start_pos = self._joint_angles.copy()
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else:
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self._joint_angles = self._start_pos
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self._generate_goal()
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self._angle_velocity = self._start_vel
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self._joints = np.zeros((self.n_links + 1, 2))
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self._update_joints()
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self._steps = 0
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return self._get_obs().copy()
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def _update_joints(self):
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"""
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update joints to get new end-effector position. The other links are only required for rendering.
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Returns:
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"""
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angles = np.cumsum(self._joint_angles)
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x = self.link_lengths * np.vstack([np.cos(angles), np.sin(angles)])
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self._joints[1:] = self._joints[0] + np.cumsum(x.T, axis=0)
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return super().reset()
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def _get_reward(self, action: np.ndarray):
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diff = self.end_effector - self._goal
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reward_dist = 0
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if not self.allow_self_collision:
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self._is_collided = self._check_self_collision()
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if self._steps >= self.steps_before_reward:
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reward_dist -= np.linalg.norm(diff)
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# reward_dist = np.exp(-0.1 * diff ** 2).mean()
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@@ -132,6 +62,9 @@ class SimpleReacherEnv(gym.Env):
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reward = reward_dist - reward_ctrl
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return reward, dict(reward_dist=reward_dist, reward_ctrl=reward_ctrl)
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def _terminate(self, info):
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return False
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def _get_obs(self):
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theta = self._joint_angles
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return np.hstack([
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@@ -190,13 +123,14 @@ class SimpleReacherEnv(gym.Env):
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self.fig.canvas.draw()
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self.fig.canvas.flush_events()
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def seed(self, seed=None):
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self.np_random, seed = seeding.np_random(seed)
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return [seed]
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def close(self):
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del self.fig
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if __name__ == "__main__":
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env = SimpleReacherEnv(5)
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env.reset()
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for i in range(200):
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ac = env.action_space.sample()
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obs, rew, done, info = env.step(ac)
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@property
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def end_effector(self):
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return self._joints[self.n_links].T
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env.render()
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if done:
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break
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