143 lines
5.1 KiB
Python
143 lines
5.1 KiB
Python
import os
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from gym.envs.mujoco.hopper_v3 import HopperEnv
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import numpy as np
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MAX_EPISODE_STEPS_HOPPERTHROWINBASKET = 250
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class ALRHopperThrowInBasketEnv(HopperEnv):
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"""
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Initialization changes to normal Hopper:
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- healthy_reward: 1.0 -> 0.0
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- healthy_angle_range: (-0.2, 0.2) -> (-float('inf'), float('inf'))
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- hit_basket_reward: - -> 10
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Reward changes to normal Hopper:
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- velocity: (x_position_after - x_position_before) -> (ball_position_after - ball_position_before)
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"""
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def __init__(self,
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xml_file='hopper_throw_in_basket.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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hit_basket_reward=10,
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basket_size=0.3,
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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=(0.7, 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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context=True,
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penalty=0.0,
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exclude_current_positions_from_observation=True,
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max_episode_steps = 250):
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self.hit_basket_reward = hit_basket_reward
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self.current_step = 0
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self.max_episode_steps = max_episode_steps
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self.ball_in_basket = False
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self.basket_size = basket_size
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self.context = context
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self.penalty = penalty
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self.basket_x = 5
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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_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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ball_pos = self.get_body_com("ball")
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basket_pos = self.get_body_com("basket_ground")
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basket_center = (basket_pos[0] + 0.5, basket_pos[1], basket_pos[2])
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is_in_basket_x = ball_pos[0] >= basket_pos[0] and ball_pos[0] <= basket_pos[0] + self.basket_size
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is_in_basket_y = ball_pos[1] >= basket_pos[1] - (self.basket_size/2) and ball_pos[1] <= basket_pos[1] + (self.basket_size/2)
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is_in_basket_z = ball_pos[2] < 0.1
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is_in_basket = is_in_basket_x and is_in_basket_y and is_in_basket_z
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if is_in_basket: self.ball_in_basket = True
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ball_landed = self.get_body_com("ball")[2] <= 0.05
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done = ball_landed or is_in_basket
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rewards = 0
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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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if is_in_basket:
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if not self.context:
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rewards += self.hit_basket_reward
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else:
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dist = np.linalg.norm(ball_pos-basket_center)
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if self.context:
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rewards = -10 * dist
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else:
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rewards -= (dist*dist)
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else:
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# penalty not needed
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rewards += ((action[:2] > 0) * self.penalty).sum() if self.current_step < 10 else 0 #too much of a penalty?
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observation = self._get_obs()
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reward = rewards - costs
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info = {
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'ball_pos': ball_pos[0],
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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.basket_x)
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def reset(self):
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if self.max_episode_steps == 10:
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# We have to initialize this here, because the spec is only added after creating the env.
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self.max_episode_steps = self.spec.max_episode_steps
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self.current_step = 0
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self.ball_in_basket = False
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if self.context:
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basket_id = self.sim.model.body_name2id("basket_ground")
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self.basket_x = np.random.uniform(3, 7, 1)
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self.sim.model.body_pos[basket_id] = [self.basket_x, 0, 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 = ALRHopperThrowInBasketEnv()
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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() |