195 lines
7.4 KiB
Python
195 lines
7.4 KiB
Python
from gym.envs.mujoco.hopper_v3 import HopperEnv
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import numpy as np
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MAX_EPISODE_STEPS_HOPPERJUMP = 250
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class ALRHopperJumpEnv(HopperEnv):
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"""
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Initialization changes to normal Hopper:
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- healthy_reward: 1.0 -> 0.1 -> 0
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- healthy_angle_range: (-0.2, 0.2) -> (-float('inf'), float('inf'))
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- healthy_z_range: (0.7, float('inf')) -> (0.5, float('inf'))
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"""
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def __init__(self,
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xml_file='hopper.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=0.0,
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penalty=0.0,
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context=True,
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terminate_when_unhealthy=True,
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healthy_state_range=(-100.0, 100.0),
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healthy_z_range=(0.5, float('inf')),
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healthy_angle_range=(-float('inf'), float('inf')),
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reset_noise_scale=5e-3,
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exclude_current_positions_from_observation=True,
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max_episode_steps=250):
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self.current_step = 0
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self.max_height = 0
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self.max_episode_steps = max_episode_steps
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self.penalty = penalty
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self.goal = 0
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self.context = context
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self.exclude_current_positions_from_observation = exclude_current_positions_from_observation
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super().__init__(xml_file, forward_reward_weight, ctrl_cost_weight, healthy_reward, terminate_when_unhealthy,
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healthy_state_range, 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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height_after = self.get_body_com("torso")[2]
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self.max_height = max(height_after, self.max_height)
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ctrl_cost = self.control_cost(action)
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costs = ctrl_cost
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done = False
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if self.current_step >= self.max_episode_steps:
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hight_goal_distance = -10*np.linalg.norm(self.max_height - self.goal) if self.context else self.max_height
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healthy_reward = 0 if self.context else self.healthy_reward * self.current_step
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height_reward = self._forward_reward_weight * hight_goal_distance # maybe move reward calculation into if structure and define two different _forward_reward_weight variables for context and episodic seperatley
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rewards = height_reward + healthy_reward
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else:
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# penalty for wrong start direction of first two joints; not needed, could be removed
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rewards = ((action[:2] > 0) * self.penalty).sum() if self.current_step < 10 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_after,
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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.goal = np.random.uniform(1.4, 2.3, 1) # 1.3 2.3
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self.max_height = 0
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self.current_step = 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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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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class ALRHopperJumpRndmPosEnv(ALRHopperJumpEnv):
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def __init__(self, max_episode_steps=250):
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self.contact_with_floor = False
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self._floor_geom_id = None
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self._foot_geom_id = None
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super(ALRHopperJumpRndmPosEnv, self).__init__(exclude_current_positions_from_observation=False,
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reset_noise_scale=5e-1,
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max_episode_steps=max_episode_steps)
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def reset_model(self):
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self._floor_geom_id = self.model.geom_name2id('floor')
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self._foot_geom_id = self.model.geom_name2id('foot_geom')
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noise_low = -np.ones(self.model.nq)*self._reset_noise_scale
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noise_low[1] = 0
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noise_low[2] = -0.3
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noise_low[3] = -0.1
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noise_low[4] = -1.1
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noise_low[5] = -0.785
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noise_high = np.ones(self.model.nq)*self._reset_noise_scale
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noise_high[1] = 0
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noise_high[2] = 0.3
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noise_high[3] = 0
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noise_high[4] = 0
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noise_high[5] = 0.785
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rnd_vec = self.np_random.uniform(low=noise_low, high=noise_high, size=self.model.nq)
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# rnd_vec[2] *= 0.05 # the angle around the y axis shouldn't be too high as the agent then falls down quickly and
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# can not recover
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# rnd_vec[1] = np.clip(rnd_vec[1], 0, 0.3)
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qpos = self.init_qpos + rnd_vec
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qvel = self.init_qvel
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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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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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self.contact_with_floor = self._contact_checker(self._floor_geom_id, self._foot_geom_id) if not \
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self.contact_with_floor else True
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height_after = self.get_body_com("torso")[2]
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self.max_height = max(height_after, self.max_height) if self.contact_with_floor else 0
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ctrl_cost = self.control_cost(action)
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costs = ctrl_cost
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done = False
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if self.current_step >= self.max_episode_steps:
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healthy_reward = 0
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height_reward = self._forward_reward_weight * self.max_height # maybe move reward calculation into if structure and define two different _forward_reward_weight variables for context and episodic seperatley
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rewards = height_reward + healthy_reward
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else:
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# penalty for wrong start direction of first two joints; not needed, could be removed
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rewards = ((action[:2] > 0) * self.penalty).sum() if self.current_step < 10 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_after,
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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 _contact_checker(self, id_1, id_2):
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for coni in range(0, self.sim.data.ncon):
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con = self.sim.data.contact[coni]
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collision = con.geom1 == id_1 and con.geom2 == id_2
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collision_trans = con.geom1 == id_2 and con.geom2 == id_1
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if collision or collision_trans:
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return True
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return False
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if __name__ == '__main__':
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render_mode = "human" # "human" or "partial" or "final"
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# env = ALRHopperJumpEnv()
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env = ALRHopperJumpRndmPosEnv()
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obs = env.reset()
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for k in range(10):
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obs = env.reset()
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print('observation :', obs[:6])
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for i in range(200):
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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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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() |