prepare HJ for PPO
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Onur
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719b40c4e4
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c47845c0dd
@ -974,7 +974,7 @@ register(
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entry_point='alr_envs.utils.make_env_helpers:make_mp_env_helper',
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kwargs={
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"name": f"alr_envs:ALRHopperJump-v3",
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"wrappers": [mujoco.hopper_jump.NewMPWrapper],
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"wrappers": [mujoco.hopper_jump.NewHighCtxtMPWrapper],
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"ep_wrapper_kwargs": {
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"weight_scale": 1
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},
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@ -1010,7 +1010,7 @@ register(
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entry_point='alr_envs.utils.make_env_helpers:make_mp_env_helper',
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kwargs={
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"name": f"alr_envs:ALRHopperJump-v4",
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"wrappers": [mujoco.hopper_jump.NewMPWrapper],
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"wrappers": [mujoco.hopper_jump.NewHighCtxtMPWrapper],
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"ep_wrapper_kwargs": {
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"weight_scale": 1
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},
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@ -11,7 +11,7 @@ class ALRHopperJumpEnv(HopperEnv):
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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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- exclude current positions from observatiosn is set to False
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"""
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def __init__(self,
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@ -26,7 +26,7 @@ class ALRHopperJumpEnv(HopperEnv):
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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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exclude_current_positions_from_observation=False,
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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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@ -90,7 +90,7 @@ class ALRHopperJumpEnv(HopperEnv):
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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, 2.16, 1) # 1.3 2.3
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self.goal = self.np_random.uniform(1.4, 2.16, 1)[0] # 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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@ -149,12 +149,12 @@ class ALRHopperXYJumpEnv(ALRHopperJumpEnv):
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if self.contact_dist is None and self.contact_with_floor:
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self.contact_dist = np.linalg.norm(self.sim.data.site_xpos[self.model.site_name2id('foot_site')]
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- np.array([self.goal, 0, 0], dtype=object))[0]
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- np.array([self.goal, 0, 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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goal_dist = np.atleast_1d(np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0], dtype=object)))[0]
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goal_dist = np.linalg.norm(site_pos_after - np.array([self.goal, 0, 0]))
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rewards = 0
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if self.current_step >= self.max_episode_steps:
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# healthy_reward = 0 if self.context else self.healthy_reward * self.current_step
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@ -210,10 +210,14 @@ class ALRHopperXYJumpEnv(ALRHopperJumpEnv):
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# self.goal = np.random.uniform(-1.5, 1.5, 1)
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# self.goal = np.random.uniform(0, 1.5, 1)
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# self.goal = self.np_random.uniform(0, 1.5, 1)
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self.goal = self.np_random.uniform(0.3, 1.35, 1)
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self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = np.array([self.goal, 0, 0], dtype=object)
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self.goal = self.np_random.uniform(0.3, 1.35, 1)[0]
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self.sim.model.body_pos[self.sim.model.body_name2id('goal_site_body')] = np.array([self.goal, 0, 0])
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return self.reset_model()
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def _get_obs(self):
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goal_diff = self.sim.data.site_xpos[self.model.site_name2id('foot_site')].copy() \
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- np.array([self.goal, 0, 0])
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return np.concatenate((super(ALRHopperXYJumpEnv, self)._get_obs(), goal_diff))
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class ALRHopperXYJumpEnvStepBased(ALRHopperXYJumpEnv):
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@ -229,7 +233,7 @@ class ALRHopperXYJumpEnvStepBased(ALRHopperXYJumpEnv):
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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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exclude_current_positions_from_observation=False,
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max_episode_steps=250,
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height_scale = 10,
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dist_scale = 3,
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@ -242,7 +246,10 @@ class ALRHopperXYJumpEnvStepBased(ALRHopperXYJumpEnv):
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reset_noise_scale, exclude_current_positions_from_observation, max_episode_steps)
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def step(self, action):
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print("")
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print('height_scale: ', self.height_scale)
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print('healthy_scale: ', self.healthy_scale)
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print('dist_scale: ', self.dist_scale)
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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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@ -348,7 +355,9 @@ 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 = ALRHopperXYJumpEnv()
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np.random.seed(0)
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env = ALRHopperXYJumpEnvStepBased()
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env.seed(0)
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# env = ALRHopperJumpRndmPosEnv()
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obs = env.reset()
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@ -33,7 +33,9 @@ class NewMPWrapper(EpisodicWrapper):
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class NewHighCtxtMPWrapper(NewMPWrapper):
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def set_active_obs(self):
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return np.hstack([
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[True] * (5 + int(not self.env.exclude_current_positions_from_observation)), # position
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[False] * (2 + int(not self.env.exclude_current_positions_from_observation)), # position
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[True] * 3, # set to true if randomize initial pos
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[False] * 6, # velocity
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[False]
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[True], # goal
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[False] * 3 # goal diff
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])
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