185 lines
7.6 KiB
Python
185 lines
7.6 KiB
Python
import os
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import numpy as np
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from gymnasium import utils
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from gymnasium.envs.mujoco import MujocoEnv
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from fancy_gym.envs.mujoco.beerpong.deprecated.beerpong_reward_staged import BeerPongReward
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class BeerPongEnv(MujocoEnv, utils.EzPickle):
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def __init__(self, frame_skip=2):
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self._steps = 0
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# Small Context -> Easier. Todo: Should we do different versions?
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# self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "assets",
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# "beerpong_wo_cup" + ".xml")
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# self._cup_pos_min = np.array([-0.32, -2.2])
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# self._cup_pos_max = np.array([0.32, -1.2])
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self.xml_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "../assets",
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"beerpong_wo_cup_big_table" + ".xml")
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self._cup_pos_min = np.array([-1.42, -4.05])
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self._cup_pos_max = np.array([1.42, -1.25])
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self._start_pos = np.array([0.0, 1.35, 0.0, 1.18, 0.0, -0.786, -1.59])
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self._start_vel = np.zeros(7)
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self.release_step = 100 # time step of ball release
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self.ep_length = 600 // frame_skip
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self.reward_function = BeerPongReward()
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self.repeat_action = frame_skip
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self.model = None
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self.site_id = lambda x: self.model.site_name2id(x)
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self.body_id = lambda x: self.model.body_name2id(x)
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MujocoEnv.__init__(self, self.xml_path, frame_skip=1)
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utils.EzPickle.__init__(self)
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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 start_vel(self):
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return self._start_vel
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def reset(self):
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self.reward_function.reset()
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return super().reset()
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def reset_model(self):
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init_pos_all = self.init_qpos.copy()
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init_pos_robot = self.start_pos
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init_vel = np.zeros_like(init_pos_all)
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self._steps = 0
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start_pos = init_pos_all
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start_pos[0:7] = init_pos_robot
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# TODO: Ask Max why we need to set the state twice.
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self.set_state(start_pos, init_vel)
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start_pos[7::] = self.sim.data.site_xpos[self.site_id("init_ball_pos"), :].copy()
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self.set_state(start_pos, init_vel)
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xy = self.np_random.uniform(self._cup_pos_min, self._cup_pos_max)
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xyz = np.zeros(3)
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xyz[:2] = xy
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xyz[-1] = 0.840
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self.sim.model.body_pos[self.body_id("cup_table")] = xyz
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return self._get_obs()
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def step(self, a):
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crash = False
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for _ in range(self.repeat_action):
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applied_action = a + self.sim.data.qfrc_bias[:len(a)].copy() / self.model.actuator_gear[:, 0]
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self.do_simulation(applied_action, self.frame_skip)
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self.reward_function.initialize(self)
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# self.reward_function.check_contacts(self.sim) # I assume this is not important?
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if self._steps < self.release_step:
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self.sim.data.qpos[7::] = self.sim.data.site_xpos[self.site_id("init_ball_pos"), :].copy()
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self.sim.data.qvel[7::] = self.sim.data.site_xvelp[self.site_id("init_ball_pos"), :].copy()
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crash = False
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ob = self._get_obs()
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if not crash:
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reward, reward_infos = self.reward_function.compute_reward(self, applied_action)
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is_collided = reward_infos['is_collided']
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terminated = is_collided or self._steps == self.ep_length - 1
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self._steps += 1
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else:
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reward = -30
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terminated = True
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reward_infos = {"success": False, "ball_pos": np.zeros(3), "ball_vel": np.zeros(3), "is_collided": False}
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infos = dict(
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reward=reward,
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action=a,
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q_pos=self.sim.data.qpos[0:7].ravel().copy(),
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q_vel=self.sim.data.qvel[0:7].ravel().copy(), sim_crash=crash,
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)
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infos.update(reward_infos)
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return ob, reward, terminated, infos
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def _get_obs(self):
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theta = self.sim.data.qpos.flat[:7]
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theta_dot = self.sim.data.qvel.flat[:7]
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ball_pos = self.data.get_body_xpos("ball").copy()
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cup_goal_diff_final = ball_pos - self.data.get_site_xpos("cup_goal_final_table").copy()
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cup_goal_diff_top = ball_pos - self.data.get_site_xpos("cup_goal_table").copy()
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return np.concatenate([
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np.cos(theta),
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np.sin(theta),
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theta_dot,
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cup_goal_diff_final,
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cup_goal_diff_top,
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self.sim.model.body_pos[self.body_id("cup_table")][:2].copy(),
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[self._steps],
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])
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@property
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def dt(self):
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return super(BeerPongEnv, self).dt * self.repeat_action
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class BeerPongEnvFixedReleaseStep(BeerPongEnv):
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def __init__(self, frame_skip=2):
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super().__init__(frame_skip)
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self.release_step = 62 # empirically evaluated for frame_skip=2!
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class BeerPongEnvStepBasedEpisodicReward(BeerPongEnv):
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def __init__(self, frame_skip=2):
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super().__init__(frame_skip)
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self.release_step = 62 # empirically evaluated for frame_skip=2!
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def step(self, a):
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if self._steps < self.release_step:
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return super(BeerPongEnvStepBasedEpisodicReward, self).step(a)
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else:
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reward = 0
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terminated, truncated = False, False
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while not (terminated or truncated):
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sub_ob, sub_reward, terminated, truncated, sub_infos = super(BeerPongEnvStepBasedEpisodicReward,
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self).step(np.zeros(a.shape))
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reward += sub_reward
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infos = sub_infos
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ob = sub_ob
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ob[-1] = self.release_step + 1 # Since we simulate until the end of the episode, PPO does not see the
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# internal steps and thus, the observation also needs to be set correctly
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return ob, reward, terminated, truncated, infos
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# class BeerBongEnvStepBased(BeerBongEnv):
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# def __init__(self, frame_skip=1, apply_gravity_comp=True, noisy=False, rndm_goal=False, cup_goal_pos=None):
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# super().__init__(frame_skip, apply_gravity_comp, noisy, rndm_goal, cup_goal_pos)
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# self.release_step = 62 # empirically evaluated for frame_skip=2!
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#
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# def step(self, a):
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# if self._steps < self.release_step:
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# return super(BeerBongEnvStepBased, self).step(a)
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# else:
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# reward = 0
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# done = False
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# while not done:
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# sub_ob, sub_reward, done, sub_infos = super(BeerBongEnvStepBased, self).step(np.zeros(a.shape))
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# if not done or sub_infos['sim_crash']:
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# reward += sub_reward
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# else:
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# ball_pos = self.sim.data.body_xpos[self.sim.model._body_name2id["ball"]].copy()
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# cup_goal_dist_final = np.linalg.norm(ball_pos - self.sim.data.site_xpos[
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# self.sim.model._site_name2id["cup_goal_final_table"]].copy())
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# cup_goal_dist_top = np.linalg.norm(ball_pos - self.sim.data.site_xpos[
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# self.sim.model._site_name2id["cup_goal_table"]].copy())
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# if sub_infos['success']:
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# dist_rew = -cup_goal_dist_final ** 2
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# else:
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# dist_rew = -0.5 * cup_goal_dist_final ** 2 - cup_goal_dist_top ** 2
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# reward = reward - sub_infos['action_cost'] + dist_rew
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# infos = sub_infos
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# ob = sub_ob
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# ob[-1] = self.release_step + 1 # Since we simulate until the end of the episode, PPO does not see the
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# # internal steps and thus, the observation also needs to be set correctly
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# return ob, reward, done, infos
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