113 lines
3.8 KiB
Python
113 lines
3.8 KiB
Python
import os
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from gym.envs.mujoco.walker2d_v3 import Walker2dEnv
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import numpy as np
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MAX_EPISODE_STEPS_WALKERJUMP = 300
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class ALRWalker2dJumpEnv(Walker2dEnv):
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"""
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healthy reward 1.0 -> 0.005 -> 0.0025 not from alex
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penalty 10 -> 0 not from alex
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"""
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def __init__(self,
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xml_file='walker2d.xml',
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forward_reward_weight=1.0,
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ctrl_cost_weight=1e-3,
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healthy_reward=0.0025,
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terminate_when_unhealthy=True,
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healthy_z_range=(0.8, 2.0),
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healthy_angle_range=(-1.0, 1.0),
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reset_noise_scale=5e-3,
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penalty=0,
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context=True,
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exclude_current_positions_from_observation=True,
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max_episode_steps=300):
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self.current_step = 0
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self.max_episode_steps = max_episode_steps
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self.max_height = 0
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self._penalty = penalty
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self.context = context
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self.goal = 0
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xml_file = os.path.join(os.path.dirname(__file__), "assets", xml_file)
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super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
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healthy_z_range, healthy_angle_range, reset_noise_scale,
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exclude_current_positions_from_observation)
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def step(self, action):
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self.current_step += 1
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self.do_simulation(action, self.frame_skip)
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#pos_after = self.get_body_com("torso")[0]
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height = self.get_body_com("torso")[2]
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self.max_height = max(height, self.max_height)
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fell_over = height < 0.2
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done = fell_over
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ctrl_cost = self.control_cost(action)
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costs = ctrl_cost
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if self.current_step >= self.max_episode_steps or done:
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done = True
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height_goal_distance = -10 * (np.linalg.norm(self.max_height - self.goal)) if self.context else self.max_height
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healthy_reward = self.healthy_reward * self.current_step
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rewards = height_goal_distance + healthy_reward
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else:
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# penalty not needed
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rewards = 0
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rewards += ((action[:2] > 0) * self._penalty).sum() if self.current_step < 4 else 0
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rewards += ((action[3:5] > 0) * self._penalty).sum() if self.current_step < 4 else 0
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observation = self._get_obs()
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reward = rewards - costs
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info = {
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'height': height,
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'max_height': self.max_height,
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'goal' : self.goal,
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}
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return observation, reward, done, info
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def _get_obs(self):
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return np.append(super()._get_obs(), self.goal)
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def reset(self):
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self.current_step = 0
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self.max_height = 0
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self.goal = np.random.uniform(1.5, 2.5, 1) # 1.5 3.0
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return super().reset()
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# overwrite reset_model to make it deterministic
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def reset_model(self):
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noise_low = -self._reset_noise_scale
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noise_high = self._reset_noise_scale
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qpos = self.init_qpos # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
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qvel = self.init_qvel # + self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nv)
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self.set_state(qpos, qvel)
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observation = self._get_obs()
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return observation
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if __name__ == '__main__':
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render_mode = "human" # "human" or "partial" or "final"
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env = ALRWalker2dJumpEnv()
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obs = env.reset()
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for i in range(2000):
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# objective.load_result("/tmp/cma")
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# test with random actions
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ac = env.action_space.sample()
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obs, rew, d, info = env.step(ac)
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if i % 10 == 0:
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env.render(mode=render_mode)
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if d:
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print('After ', i, ' steps, done: ', d)
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env.reset()
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env.close() |