naming convention and running tests
This commit is contained in:
Fabian
+338
-325
@@ -2,7 +2,7 @@ from copy import deepcopy
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from . import manipulation, suite
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ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": []}
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ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS = {"DMP": [], "ProMP": []}
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from gym.envs.registration import register
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@@ -47,10 +47,9 @@ DEFAULT_BB_DICT_DMP = {
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}
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}
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# DeepMind Control Suite (DMC)
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kwargs_dict_bic_dmp = deepcopy(DEFAULT_BB_DICT_DMP)
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kwargs_dict_bic_dmp['name'] = f"ball_in_cup-catch"
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kwargs_dict_bic_dmp['name'] = f"dmc:ball_in_cup-catch"
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kwargs_dict_bic_dmp['wrappers'].append(suite.ball_in_cup.MPWrapper)
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kwargs_dict_bic_dmp['phase_generator_kwargs']['alpha_phase'] = 2
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kwargs_dict_bic_dmp['trajectory_generator_kwargs']['weight_scale'] = 10 # TODO: weight scale 1, but goal scale 0.1
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@@ -58,304 +57,313 @@ kwargs_dict_bic_dmp['controller_kwargs']['p_gains'] = 50
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kwargs_dict_bic_dmp['controller_kwargs']['d_gains'] = 1
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register(
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id=f'dmc_ball_in_cup-catch_dmp-v0',
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entry_point='alr_envs.utils.make_env_helpers:make_dmp_env_helper',
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entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
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# max_episode_steps=1,
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kwargs={
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"name": f"ball_in_cup-catch",
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"time_limit": 20,
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"episode_length": 1000,
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"wrappers": [suite.ball_in_cup.MPWrapper],
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"traj_gen_kwargs": {
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"num_dof": 2,
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"num_basis": 5,
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"duration": 20,
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"learn_goal": True,
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"alpha_phase": 2,
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"bandwidth_factor": 2,
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"policy_type": "motor",
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"goal_scale": 0.1,
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"policy_kwargs": {
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"p_gains": 50,
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"d_gains": 1
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}
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}
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}
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kwargs=kwargs_dict_bic_dmp
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# {
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# "name": f"ball_in_cup-catch",
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# "time_limit": 20,
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# "episode_length": 1000,
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# "wrappers": [suite.ball_in_cup.MPWrapper],
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# "traj_gen_kwargs": {
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# "num_dof": 2,
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# "num_basis": 5,
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# "duration": 20,
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# "learn_goal": True,
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# "alpha_phase": 2,
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# "bandwidth_factor": 2,
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# "policy_type": "motor",
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# "goal_scale": 0.1,
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# "policy_kwargs": {
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# "p_gains": 50,
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# "d_gains": 1
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# }
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# }
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# }
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)
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ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"].append("dmc_ball_in_cup-catch_dmp-v0")
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ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["DMP"].append("dmc_ball_in_cup-catch_dmp-v0")
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kwargs_dict_bic_promp = deepcopy(DEFAULT_BB_DICT_DMP)
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kwargs_dict_bic_promp['name'] = f"ball_in_cup-catch"
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kwargs_dict_bic_promp['name'] = f"dmc:ball_in_cup-catch"
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kwargs_dict_bic_promp['wrappers'].append(suite.ball_in_cup.MPWrapper)
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kwargs_dict_bic_promp['controller_kwargs']['p_gains'] = 50
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kwargs_dict_bic_promp['controller_kwargs']['d_gains'] = 1
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register(
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id=f'dmc_ball_in_cup-catch_promp-v0',
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entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
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kwargs={
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"name": f"ball_in_cup-catch",
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"time_limit": 20,
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"episode_length": 1000,
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"wrappers": [suite.ball_in_cup.MPWrapper],
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"traj_gen_kwargs": {
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"num_dof": 2,
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"num_basis": 5,
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"duration": 20,
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"policy_type": "motor",
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"zero_start": True,
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"policy_kwargs": {
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"p_gains": 50,
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"d_gains": 1
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}
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}
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}
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entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
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kwargs=kwargs_dict_bic_promp
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# {
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# "name": f"ball_in_cup-catch",
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# "time_limit": 20,
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# "episode_length": 1000,
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# "wrappers": [suite.ball_in_cup.MPWrapper],
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# "traj_gen_kwargs": {
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# "num_dof": 2,
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# "num_basis": 5,
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# "duration": 20,
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# "policy_type": "motor",
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# "zero_start": True,
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# "policy_kwargs": {
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# "p_gains": 50,
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# "d_gains": 1
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# }
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# }
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# }
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)
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ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append("dmc_ball_in_cup-catch_promp-v0")
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ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append("dmc_ball_in_cup-catch_promp-v0")
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kwargs_dict_reacher_easy_dmp = deepcopy(DEFAULT_BB_DICT_DMP)
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kwargs_dict_reacher_easy_dmp['name'] = f"reacher-easy"
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kwargs_dict_reacher_easy_dmp['name'] = f"dmc:reacher-easy"
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kwargs_dict_reacher_easy_dmp['wrappers'].append(suite.reacher.MPWrapper)
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kwargs_dict_reacher_easy_dmp['phase_generator_kwargs']['alpha_phase'] = 2
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kwargs_dict_reacher_easy_dmp['trajectory_generator_kwargs']['weight_scale'] = 500 # TODO: weight scale 50, but goal scale 0.1
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# TODO: weight scale 50, but goal scale 0.1
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kwargs_dict_reacher_easy_dmp['trajectory_generator_kwargs']['weight_scale'] = 500
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kwargs_dict_reacher_easy_dmp['controller_kwargs']['p_gains'] = 50
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kwargs_dict_reacher_easy_dmp['controller_kwargs']['d_gains'] = 1
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register(
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id=f'dmc_reacher-easy_dmp-v0',
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entry_point='alr_envs.utils.make_env_helpers:make_dmp_env_helper',
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entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
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# max_episode_steps=1,
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kwargs={
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"name": f"reacher-easy",
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"time_limit": 20,
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"episode_length": 1000,
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"wrappers": [suite.reacher.MPWrapper],
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"traj_gen_kwargs": {
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"num_dof": 2,
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"num_basis": 5,
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"duration": 20,
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"learn_goal": True,
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"alpha_phase": 2,
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"bandwidth_factor": 2,
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"policy_type": "motor",
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"weights_scale": 50,
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"goal_scale": 0.1,
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"policy_kwargs": {
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"p_gains": 50,
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"d_gains": 1
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}
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}
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}
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kwargs=kwargs_dict_bic_dmp
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# {
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# "name": f"reacher-easy",
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# "time_limit": 20,
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# "episode_length": 1000,
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# "wrappers": [suite.reacher.MPWrapper],
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# "traj_gen_kwargs": {
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# "num_dof": 2,
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# "num_basis": 5,
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# "duration": 20,
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# "learn_goal": True,
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# "alpha_phase": 2,
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# "bandwidth_factor": 2,
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# "policy_type": "motor",
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# "weights_scale": 50,
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# "goal_scale": 0.1,
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# "policy_kwargs": {
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# "p_gains": 50,
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# "d_gains": 1
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# }
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# }
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# }
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)
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ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"].append("dmc_reacher-easy_dmp-v0")
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ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["DMP"].append("dmc_reacher-easy_dmp-v0")
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kwargs_dict_reacher_easy_promp = deepcopy(DEFAULT_BB_DICT_DMP)
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kwargs_dict_reacher_easy_promp['name'] = f"reacher-easy"
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kwargs_dict_reacher_easy_promp['name'] = f"dmc:reacher-easy"
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kwargs_dict_reacher_easy_promp['wrappers'].append(suite.reacher.MPWrapper)
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kwargs_dict_reacher_easy_promp['controller_kwargs']['p_gains'] = 50
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kwargs_dict_reacher_easy_promp['controller_kwargs']['d_gains'] = 1
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kwargs_dict_reacher_easy_promp['trajectory_generator_kwargs']['weight_scale'] = 0.2
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register(
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id=f'dmc_reacher-easy_promp-v0',
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entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
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kwargs={
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"name": f"reacher-easy",
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"time_limit": 20,
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"episode_length": 1000,
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"wrappers": [suite.reacher.MPWrapper],
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"traj_gen_kwargs": {
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"num_dof": 2,
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"num_basis": 5,
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"duration": 20,
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"policy_type": "motor",
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"weights_scale": 0.2,
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"zero_start": True,
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"policy_kwargs": {
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"p_gains": 50,
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"d_gains": 1
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}
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}
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}
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entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
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kwargs=kwargs_dict_reacher_easy_promp
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# {
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# "name": f"reacher-easy",
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# "time_limit": 20,
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# "episode_length": 1000,
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# "wrappers": [suite.reacher.MPWrapper],
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# "traj_gen_kwargs": {
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# "num_dof": 2,
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# "num_basis": 5,
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# "duration": 20,
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# "policy_type": "motor",
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# "weights_scale": 0.2,
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# "zero_start": True,
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# "policy_kwargs": {
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# "p_gains": 50,
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# "d_gains": 1
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# }
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# }
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# }
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)
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ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append("dmc_reacher-easy_promp-v0")
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ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append("dmc_reacher-easy_promp-v0")
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kwargs_dict_reacher_hard_dmp = deepcopy(DEFAULT_BB_DICT_DMP)
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kwargs_dict_reacher_hard_dmp['name'] = f"reacher-hard"
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kwargs_dict_reacher_hard_dmp['name'] = f"dmc:reacher-hard"
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kwargs_dict_reacher_hard_dmp['wrappers'].append(suite.reacher.MPWrapper)
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kwargs_dict_reacher_hard_dmp['phase_generator_kwargs']['alpha_phase'] = 2
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kwargs_dict_reacher_hard_dmp['trajectory_generator_kwargs']['weight_scale'] = 500 # TODO: weight scale 50, but goal scale 0.1
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# TODO: weight scale 50, but goal scale 0.1
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kwargs_dict_reacher_hard_dmp['trajectory_generator_kwargs']['weight_scale'] = 500
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kwargs_dict_reacher_hard_dmp['controller_kwargs']['p_gains'] = 50
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kwargs_dict_reacher_hard_dmp['controller_kwargs']['d_gains'] = 1
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register(
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id=f'dmc_reacher-hard_dmp-v0',
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entry_point='alr_envs.utils.make_env_helpers:make_dmp_env_helper',
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entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
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# max_episode_steps=1,
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kwargs={
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"name": f"reacher-hard",
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"time_limit": 20,
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"episode_length": 1000,
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"wrappers": [suite.reacher.MPWrapper],
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"traj_gen_kwargs": {
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"num_dof": 2,
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"num_basis": 5,
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"duration": 20,
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"learn_goal": True,
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"alpha_phase": 2,
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"bandwidth_factor": 2,
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"policy_type": "motor",
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"weights_scale": 50,
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"goal_scale": 0.1,
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"policy_kwargs": {
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"p_gains": 50,
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"d_gains": 1
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}
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}
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}
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kwargs=kwargs_dict_reacher_hard_dmp
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# {
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# "name": f"reacher-hard",
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# "time_limit": 20,
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# "episode_length": 1000,
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# "wrappers": [suite.reacher.MPWrapper],
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# "traj_gen_kwargs": {
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# "num_dof": 2,
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# "num_basis": 5,
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# "duration": 20,
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# "learn_goal": True,
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# "alpha_phase": 2,
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# "bandwidth_factor": 2,
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# "policy_type": "motor",
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# "weights_scale": 50,
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# "goal_scale": 0.1,
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# "policy_kwargs": {
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# "p_gains": 50,
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# "d_gains": 1
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# }
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# }
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# }
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)
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ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"].append("dmc_reacher-hard_dmp-v0")
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ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["DMP"].append("dmc_reacher-hard_dmp-v0")
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kwargs_dict_reacher_hard_promp = deepcopy(DEFAULT_BB_DICT_DMP)
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kwargs_dict_reacher_hard_promp['name'] = f"reacher-hard"
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kwargs_dict_reacher_hard_promp['name'] = f"dmc:reacher-hard"
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kwargs_dict_reacher_hard_promp['wrappers'].append(suite.reacher.MPWrapper)
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kwargs_dict_reacher_hard_promp['controller_kwargs']['p_gains'] = 50
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kwargs_dict_reacher_hard_promp['controller_kwargs']['d_gains'] = 1
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kwargs_dict_reacher_hard_promp['trajectory_generator_kwargs']['weight_scale'] = 0.2
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register(
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id=f'dmc_reacher-hard_promp-v0',
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entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
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kwargs={
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"name": f"reacher-hard",
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"time_limit": 20,
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"episode_length": 1000,
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"wrappers": [suite.reacher.MPWrapper],
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"traj_gen_kwargs": {
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"num_dof": 2,
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"num_basis": 5,
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"duration": 20,
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"policy_type": "motor",
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"weights_scale": 0.2,
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"zero_start": True,
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"policy_kwargs": {
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"p_gains": 50,
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"d_gains": 1
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}
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}
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}
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entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
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kwargs=kwargs_dict_reacher_hard_promp
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# {
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# "name": f"reacher-hard",
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# "time_limit": 20,
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# "episode_length": 1000,
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# "wrappers": [suite.reacher.MPWrapper],
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# "traj_gen_kwargs": {
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# "num_dof": 2,
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# "num_basis": 5,
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# "duration": 20,
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# "policy_type": "motor",
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# "weights_scale": 0.2,
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# "zero_start": True,
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# "policy_kwargs": {
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# "p_gains": 50,
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# "d_gains": 1
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# }
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# }
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# }
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)
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ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append("dmc_reacher-hard_promp-v0")
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ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append("dmc_reacher-hard_promp-v0")
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_dmc_cartpole_tasks = ["balance", "balance_sparse", "swingup", "swingup_sparse"]
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for _task in _dmc_cartpole_tasks:
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_env_id = f'dmc_cartpole-{_task}_dmp-v0'
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kwargs_dict_cartpole_dmp = deepcopy(DEFAULT_BB_DICT_DMP)
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kwargs_dict_cartpole_dmp['name'] = f"cartpole-{_task}"
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kwargs_dict_cartpole_dmp['camera_id'] = 0
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kwargs_dict_cartpole_dmp['name'] = f"dmc:cartpole-{_task}"
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kwargs_dict_cartpole_dmp['wrappers'].append(suite.cartpole.MPWrapper)
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kwargs_dict_cartpole_dmp['phase_generator_kwargs']['alpha_phase'] = 2
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kwargs_dict_cartpole_dmp['trajectory_generator_kwargs']['weight_scale'] = 500 # TODO: weight scale 50, but goal scale 0.1
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kwargs_dict_cartpole_dmp['trajectory_generator_kwargs'][
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'weight_scale'] = 500 # TODO: weight scale 50, but goal scale 0.1
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kwargs_dict_cartpole_dmp['controller_kwargs']['p_gains'] = 10
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kwargs_dict_cartpole_dmp['controller_kwargs']['d_gains'] = 10
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register(
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id=_env_id,
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entry_point='alr_envs.utils.make_env_helpers:make_dmp_env_helper',
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entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
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# max_episode_steps=1,
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kwargs={
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"name": f"cartpole-{_task}",
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# "time_limit": 1,
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"camera_id": 0,
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"episode_length": 1000,
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"wrappers": [suite.cartpole.MPWrapper],
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"traj_gen_kwargs": {
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"num_dof": 1,
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"num_basis": 5,
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"duration": 10,
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"learn_goal": True,
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"alpha_phase": 2,
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"bandwidth_factor": 2,
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"policy_type": "motor",
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"weights_scale": 50,
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"goal_scale": 0.1,
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"policy_kwargs": {
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"p_gains": 10,
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"d_gains": 10
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}
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}
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}
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kwargs=kwargs_dict_cartpole_dmp
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# {
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# "name": f"cartpole-{_task}",
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# # "time_limit": 1,
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# "camera_id": 0,
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# "episode_length": 1000,
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# "wrappers": [suite.cartpole.MPWrapper],
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# "traj_gen_kwargs": {
|
||||
# "num_dof": 1,
|
||||
# "num_basis": 5,
|
||||
# "duration": 10,
|
||||
# "learn_goal": True,
|
||||
# "alpha_phase": 2,
|
||||
# "bandwidth_factor": 2,
|
||||
# "policy_type": "motor",
|
||||
# "weights_scale": 50,
|
||||
# "goal_scale": 0.1,
|
||||
# "policy_kwargs": {
|
||||
# "p_gains": 10,
|
||||
# "d_gains": 10
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
)
|
||||
ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"].append(_env_id)
|
||||
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["DMP"].append(_env_id)
|
||||
|
||||
_env_id = f'dmc_cartpole-{_task}_promp-v0'
|
||||
kwargs_dict_cartpole_promp = deepcopy(DEFAULT_BB_DICT_DMP)
|
||||
kwargs_dict_cartpole_promp['name'] = f"cartpole-{_task}"
|
||||
kwargs_dict_cartpole_promp['camera_id'] = 0
|
||||
kwargs_dict_cartpole_promp['name'] = f"dmc:cartpole-{_task}"
|
||||
kwargs_dict_cartpole_promp['wrappers'].append(suite.cartpole.MPWrapper)
|
||||
kwargs_dict_cartpole_promp['controller_kwargs']['p_gains'] = 10
|
||||
kwargs_dict_cartpole_promp['controller_kwargs']['d_gains'] = 10
|
||||
kwargs_dict_cartpole_promp['trajectory_generator_kwargs']['weight_scale'] = 0.2
|
||||
register(
|
||||
id=_env_id,
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
|
||||
kwargs={
|
||||
"name": f"cartpole-{_task}",
|
||||
# "time_limit": 1,
|
||||
"camera_id": 0,
|
||||
"episode_length": 1000,
|
||||
"wrappers": [suite.cartpole.MPWrapper],
|
||||
"traj_gen_kwargs": {
|
||||
"num_dof": 1,
|
||||
"num_basis": 5,
|
||||
"duration": 10,
|
||||
"policy_type": "motor",
|
||||
"weights_scale": 0.2,
|
||||
"zero_start": True,
|
||||
"policy_kwargs": {
|
||||
"p_gains": 10,
|
||||
"d_gains": 10
|
||||
}
|
||||
}
|
||||
}
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
|
||||
kwargs=kwargs_dict_cartpole_promp
|
||||
# {
|
||||
# "name": f"cartpole-{_task}",
|
||||
# # "time_limit": 1,
|
||||
# "camera_id": 0,
|
||||
# "episode_length": 1000,
|
||||
# "wrappers": [suite.cartpole.MPWrapper],
|
||||
# "traj_gen_kwargs": {
|
||||
# "num_dof": 1,
|
||||
# "num_basis": 5,
|
||||
# "duration": 10,
|
||||
# "policy_type": "motor",
|
||||
# "weights_scale": 0.2,
|
||||
# "zero_start": True,
|
||||
# "policy_kwargs": {
|
||||
# "p_gains": 10,
|
||||
# "d_gains": 10
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
)
|
||||
ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
|
||||
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
|
||||
|
||||
kwargs_dict_cartpole2poles_dmp = deepcopy(DEFAULT_BB_DICT_DMP)
|
||||
kwargs_dict_cartpole2poles_dmp['name'] = f"cartpole-two_poles"
|
||||
kwargs_dict_cartpole2poles_dmp['camera_id'] = 0
|
||||
kwargs_dict_cartpole2poles_dmp['name'] = f"dmc:cartpole-two_poles"
|
||||
kwargs_dict_cartpole2poles_dmp['wrappers'].append(suite.cartpole.TwoPolesMPWrapper)
|
||||
kwargs_dict_cartpole2poles_dmp['phase_generator_kwargs']['alpha_phase'] = 2
|
||||
kwargs_dict_cartpole2poles_dmp['trajectory_generator_kwargs']['weight_scale'] = 500 # TODO: weight scale 50, but goal scale 0.1
|
||||
# TODO: weight scale 50, but goal scale 0.1
|
||||
kwargs_dict_cartpole2poles_dmp['trajectory_generator_kwargs']['weight_scale'] = 500
|
||||
kwargs_dict_cartpole2poles_dmp['controller_kwargs']['p_gains'] = 10
|
||||
kwargs_dict_cartpole2poles_dmp['controller_kwargs']['d_gains'] = 10
|
||||
_env_id = f'dmc_cartpole-two_poles_dmp-v0'
|
||||
register(
|
||||
id=_env_id,
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_dmp_env_helper',
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
|
||||
# max_episode_steps=1,
|
||||
kwargs={
|
||||
"name": f"cartpole-two_poles",
|
||||
# "time_limit": 1,
|
||||
"camera_id": 0,
|
||||
"episode_length": 1000,
|
||||
"wrappers": [suite.cartpole.TwoPolesMPWrapper],
|
||||
"traj_gen_kwargs": {
|
||||
"num_dof": 1,
|
||||
"num_basis": 5,
|
||||
"duration": 10,
|
||||
"learn_goal": True,
|
||||
"alpha_phase": 2,
|
||||
"bandwidth_factor": 2,
|
||||
"policy_type": "motor",
|
||||
"weights_scale": 50,
|
||||
"goal_scale": 0.1,
|
||||
"policy_kwargs": {
|
||||
"p_gains": 10,
|
||||
"d_gains": 10
|
||||
}
|
||||
}
|
||||
}
|
||||
kwargs=kwargs_dict_cartpole2poles_dmp
|
||||
# {
|
||||
# "name": f"cartpole-two_poles",
|
||||
# # "time_limit": 1,
|
||||
# "camera_id": 0,
|
||||
# "episode_length": 1000,
|
||||
# "wrappers": [suite.cartpole.TwoPolesMPWrapper],
|
||||
# "traj_gen_kwargs": {
|
||||
# "num_dof": 1,
|
||||
# "num_basis": 5,
|
||||
# "duration": 10,
|
||||
# "learn_goal": True,
|
||||
# "alpha_phase": 2,
|
||||
# "bandwidth_factor": 2,
|
||||
# "policy_type": "motor",
|
||||
# "weights_scale": 50,
|
||||
# "goal_scale": 0.1,
|
||||
# "policy_kwargs": {
|
||||
# "p_gains": 10,
|
||||
# "d_gains": 10
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
)
|
||||
ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"].append(_env_id)
|
||||
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["DMP"].append(_env_id)
|
||||
|
||||
kwargs_dict_cartpole2poles_promp = deepcopy(DEFAULT_BB_DICT_DMP)
|
||||
kwargs_dict_cartpole2poles_promp['name'] = f"cartpole-two_poles"
|
||||
kwargs_dict_cartpole2poles_promp['camera_id'] = 0
|
||||
kwargs_dict_cartpole2poles_promp['name'] = f"dmc:cartpole-two_poles"
|
||||
kwargs_dict_cartpole2poles_promp['wrappers'].append(suite.cartpole.TwoPolesMPWrapper)
|
||||
kwargs_dict_cartpole2poles_promp['controller_kwargs']['p_gains'] = 10
|
||||
kwargs_dict_cartpole2poles_promp['controller_kwargs']['d_gains'] = 10
|
||||
@@ -363,70 +371,71 @@ kwargs_dict_cartpole2poles_promp['trajectory_generator_kwargs']['weight_scale']
|
||||
_env_id = f'dmc_cartpole-two_poles_promp-v0'
|
||||
register(
|
||||
id=_env_id,
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
|
||||
kwargs={
|
||||
"name": f"cartpole-two_poles",
|
||||
# "time_limit": 1,
|
||||
"camera_id": 0,
|
||||
"episode_length": 1000,
|
||||
"wrappers": [suite.cartpole.TwoPolesMPWrapper],
|
||||
"traj_gen_kwargs": {
|
||||
"num_dof": 1,
|
||||
"num_basis": 5,
|
||||
"duration": 10,
|
||||
"policy_type": "motor",
|
||||
"weights_scale": 0.2,
|
||||
"zero_start": True,
|
||||
"policy_kwargs": {
|
||||
"p_gains": 10,
|
||||
"d_gains": 10
|
||||
}
|
||||
}
|
||||
}
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
|
||||
kwargs=kwargs_dict_cartpole2poles_promp
|
||||
# {
|
||||
# "name": f"cartpole-two_poles",
|
||||
# # "time_limit": 1,
|
||||
# "camera_id": 0,
|
||||
# "episode_length": 1000,
|
||||
# "wrappers": [suite.cartpole.TwoPolesMPWrapper],
|
||||
# "traj_gen_kwargs": {
|
||||
# "num_dof": 1,
|
||||
# "num_basis": 5,
|
||||
# "duration": 10,
|
||||
# "policy_type": "motor",
|
||||
# "weights_scale": 0.2,
|
||||
# "zero_start": True,
|
||||
# "policy_kwargs": {
|
||||
# "p_gains": 10,
|
||||
# "d_gains": 10
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
)
|
||||
ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
|
||||
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
|
||||
|
||||
kwargs_dict_cartpole3poles_dmp = deepcopy(DEFAULT_BB_DICT_DMP)
|
||||
kwargs_dict_cartpole3poles_dmp['name'] = f"cartpole-three_poles"
|
||||
kwargs_dict_cartpole3poles_dmp['camera_id'] = 0
|
||||
kwargs_dict_cartpole3poles_dmp['name'] = f"dmc:cartpole-three_poles"
|
||||
kwargs_dict_cartpole3poles_dmp['wrappers'].append(suite.cartpole.ThreePolesMPWrapper)
|
||||
kwargs_dict_cartpole3poles_dmp['phase_generator_kwargs']['alpha_phase'] = 2
|
||||
kwargs_dict_cartpole3poles_dmp['trajectory_generator_kwargs']['weight_scale'] = 500 # TODO: weight scale 50, but goal scale 0.1
|
||||
# TODO: weight scale 50, but goal scale 0.1
|
||||
kwargs_dict_cartpole3poles_dmp['trajectory_generator_kwargs']['weight_scale'] = 500
|
||||
kwargs_dict_cartpole3poles_dmp['controller_kwargs']['p_gains'] = 10
|
||||
kwargs_dict_cartpole3poles_dmp['controller_kwargs']['d_gains'] = 10
|
||||
_env_id = f'dmc_cartpole-three_poles_dmp-v0'
|
||||
register(
|
||||
id=_env_id,
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_dmp_env_helper',
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
|
||||
# max_episode_steps=1,
|
||||
kwargs={
|
||||
"name": f"cartpole-three_poles",
|
||||
# "time_limit": 1,
|
||||
"camera_id": 0,
|
||||
"episode_length": 1000,
|
||||
"wrappers": [suite.cartpole.ThreePolesMPWrapper],
|
||||
"traj_gen_kwargs": {
|
||||
"num_dof": 1,
|
||||
"num_basis": 5,
|
||||
"duration": 10,
|
||||
"learn_goal": True,
|
||||
"alpha_phase": 2,
|
||||
"bandwidth_factor": 2,
|
||||
"policy_type": "motor",
|
||||
"weights_scale": 50,
|
||||
"goal_scale": 0.1,
|
||||
"policy_kwargs": {
|
||||
"p_gains": 10,
|
||||
"d_gains": 10
|
||||
}
|
||||
}
|
||||
}
|
||||
kwargs=kwargs_dict_cartpole3poles_dmp
|
||||
# {
|
||||
# "name": f"cartpole-three_poles",
|
||||
# # "time_limit": 1,
|
||||
# "camera_id": 0,
|
||||
# "episode_length": 1000,
|
||||
# "wrappers": [suite.cartpole.ThreePolesMPWrapper],
|
||||
# "traj_gen_kwargs": {
|
||||
# "num_dof": 1,
|
||||
# "num_basis": 5,
|
||||
# "duration": 10,
|
||||
# "learn_goal": True,
|
||||
# "alpha_phase": 2,
|
||||
# "bandwidth_factor": 2,
|
||||
# "policy_type": "motor",
|
||||
# "weights_scale": 50,
|
||||
# "goal_scale": 0.1,
|
||||
# "policy_kwargs": {
|
||||
# "p_gains": 10,
|
||||
# "d_gains": 10
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
)
|
||||
ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"].append(_env_id)
|
||||
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["DMP"].append(_env_id)
|
||||
|
||||
kwargs_dict_cartpole3poles_promp = deepcopy(DEFAULT_BB_DICT_DMP)
|
||||
kwargs_dict_cartpole3poles_promp['name'] = f"cartpole-three_poles"
|
||||
kwargs_dict_cartpole3poles_promp['camera_id'] = 0
|
||||
kwargs_dict_cartpole3poles_promp['name'] = f"dmc:cartpole-three_poles"
|
||||
kwargs_dict_cartpole3poles_promp['wrappers'].append(suite.cartpole.ThreePolesMPWrapper)
|
||||
kwargs_dict_cartpole3poles_promp['controller_kwargs']['p_gains'] = 10
|
||||
kwargs_dict_cartpole3poles_promp['controller_kwargs']['d_gains'] = 10
|
||||
@@ -434,81 +443,85 @@ kwargs_dict_cartpole3poles_promp['trajectory_generator_kwargs']['weight_scale']
|
||||
_env_id = f'dmc_cartpole-three_poles_promp-v0'
|
||||
register(
|
||||
id=_env_id,
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
|
||||
kwargs={
|
||||
"name": f"cartpole-three_poles",
|
||||
# "time_limit": 1,
|
||||
"camera_id": 0,
|
||||
"episode_length": 1000,
|
||||
"wrappers": [suite.cartpole.ThreePolesMPWrapper],
|
||||
"traj_gen_kwargs": {
|
||||
"num_dof": 1,
|
||||
"num_basis": 5,
|
||||
"duration": 10,
|
||||
"policy_type": "motor",
|
||||
"weights_scale": 0.2,
|
||||
"zero_start": True,
|
||||
"policy_kwargs": {
|
||||
"p_gains": 10,
|
||||
"d_gains": 10
|
||||
}
|
||||
}
|
||||
}
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
|
||||
kwargs=kwargs_dict_cartpole3poles_promp
|
||||
# {
|
||||
# "name": f"cartpole-three_poles",
|
||||
# # "time_limit": 1,
|
||||
# "camera_id": 0,
|
||||
# "episode_length": 1000,
|
||||
# "wrappers": [suite.cartpole.ThreePolesMPWrapper],
|
||||
# "traj_gen_kwargs": {
|
||||
# "num_dof": 1,
|
||||
# "num_basis": 5,
|
||||
# "duration": 10,
|
||||
# "policy_type": "motor",
|
||||
# "weights_scale": 0.2,
|
||||
# "zero_start": True,
|
||||
# "policy_kwargs": {
|
||||
# "p_gains": 10,
|
||||
# "d_gains": 10
|
||||
# }
|
||||
# }
|
||||
# }
|
||||
)
|
||||
ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
|
||||
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
|
||||
|
||||
# DeepMind Manipulation
|
||||
kwargs_dict_mani_reach_site_features_dmp = deepcopy(DEFAULT_BB_DICT_DMP)
|
||||
kwargs_dict_mani_reach_site_features_dmp['name'] = f"manipulation-reach_site_features"
|
||||
kwargs_dict_mani_reach_site_features_dmp['name'] = f"dmc:manipulation-reach_site_features"
|
||||
kwargs_dict_mani_reach_site_features_dmp['wrappers'].append(manipulation.reach_site.MPWrapper)
|
||||
kwargs_dict_mani_reach_site_features_dmp['phase_generator_kwargs']['alpha_phase'] = 2
|
||||
kwargs_dict_mani_reach_site_features_dmp['trajectory_generator_kwargs']['weight_scale'] = 500 # TODO: weight scale 50, but goal scale 0.1
|
||||
# TODO: weight scale 50, but goal scale 0.1
|
||||
kwargs_dict_mani_reach_site_features_dmp['trajectory_generator_kwargs']['weight_scale'] = 500
|
||||
kwargs_dict_mani_reach_site_features_dmp['controller_kwargs']['controller_type'] = 'velocity'
|
||||
register(
|
||||
id=f'dmc_manipulation-reach_site_dmp-v0',
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_dmp_env_helper',
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
|
||||
# max_episode_steps=1,
|
||||
kwargs={
|
||||
"name": f"manipulation-reach_site_features",
|
||||
# "time_limit": 1,
|
||||
"episode_length": 250,
|
||||
"wrappers": [manipulation.reach_site.MPWrapper],
|
||||
"traj_gen_kwargs": {
|
||||
"num_dof": 9,
|
||||
"num_basis": 5,
|
||||
"duration": 10,
|
||||
"learn_goal": True,
|
||||
"alpha_phase": 2,
|
||||
"bandwidth_factor": 2,
|
||||
"policy_type": "velocity",
|
||||
"weights_scale": 50,
|
||||
"goal_scale": 0.1,
|
||||
}
|
||||
}
|
||||
kwargs=kwargs_dict_mani_reach_site_features_dmp
|
||||
# {
|
||||
# "name": f"manipulation-reach_site_features",
|
||||
# # "time_limit": 1,
|
||||
# "episode_length": 250,
|
||||
# "wrappers": [manipulation.reach_site.MPWrapper],
|
||||
# "traj_gen_kwargs": {
|
||||
# "num_dof": 9,
|
||||
# "num_basis": 5,
|
||||
# "duration": 10,
|
||||
# "learn_goal": True,
|
||||
# "alpha_phase": 2,
|
||||
# "bandwidth_factor": 2,
|
||||
# "policy_type": "velocity",
|
||||
# "weights_scale": 50,
|
||||
# "goal_scale": 0.1,
|
||||
# }
|
||||
# }
|
||||
)
|
||||
ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["DMP"].append("dmc_manipulation-reach_site_dmp-v0")
|
||||
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["DMP"].append("dmc_manipulation-reach_site_dmp-v0")
|
||||
|
||||
kwargs_dict_mani_reach_site_features_promp = deepcopy(DEFAULT_BB_DICT_DMP)
|
||||
kwargs_dict_mani_reach_site_features_promp['name'] = f"manipulation-reach_site_features"
|
||||
kwargs_dict_mani_reach_site_features_promp['name'] = f"dmc:manipulation-reach_site_features"
|
||||
kwargs_dict_mani_reach_site_features_promp['wrappers'].append(manipulation.reach_site.MPWrapper)
|
||||
kwargs_dict_mani_reach_site_features_promp['trajectory_generator_kwargs']['weight_scale'] = 0.2
|
||||
kwargs_dict_mani_reach_site_features_promp['controller_kwargs']['controller_type'] = 'velocity'
|
||||
register(
|
||||
id=f'dmc_manipulation-reach_site_promp-v0',
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_promp_env_helper',
|
||||
kwargs={
|
||||
"name": f"manipulation-reach_site_features",
|
||||
# "time_limit": 1,
|
||||
"episode_length": 250,
|
||||
"wrappers": [manipulation.reach_site.MPWrapper],
|
||||
"traj_gen_kwargs": {
|
||||
"num_dof": 9,
|
||||
"num_basis": 5,
|
||||
"duration": 10,
|
||||
"policy_type": "velocity",
|
||||
"weights_scale": 0.2,
|
||||
"zero_start": True,
|
||||
}
|
||||
}
|
||||
entry_point='alr_envs.utils.make_env_helpers:make_bb_env_helper',
|
||||
kwargs=kwargs_dict_mani_reach_site_features_promp
|
||||
# {
|
||||
# "name": f"manipulation-reach_site_features",
|
||||
# # "time_limit": 1,
|
||||
# "episode_length": 250,
|
||||
# "wrappers": [manipulation.reach_site.MPWrapper],
|
||||
# "traj_gen_kwargs": {
|
||||
# "num_dof": 9,
|
||||
# "num_basis": 5,
|
||||
# "duration": 10,
|
||||
# "policy_type": "velocity",
|
||||
# "weights_scale": 0.2,
|
||||
# "zero_start": True,
|
||||
# }
|
||||
# }
|
||||
)
|
||||
ALL_DEEPMIND_MOTION_PRIMITIVE_ENVIRONMENTS["ProMP"].append("dmc_manipulation-reach_site_promp-v0")
|
||||
ALL_DMC_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append("dmc_manipulation-reach_site_promp-v0")
|
||||
|
||||
+73
-99
@@ -2,17 +2,22 @@
|
||||
# License: MIT
|
||||
# Copyright (c) 2020 Denis Yarats
|
||||
import collections
|
||||
from typing import Any, Dict, Tuple
|
||||
from collections.abc import MutableMapping
|
||||
from typing import Any, Dict, Tuple, Optional, Union, Callable
|
||||
|
||||
from dm_control import composer
|
||||
import gym
|
||||
import numpy as np
|
||||
from dm_control import manipulation, suite
|
||||
from dm_control.rl import control
|
||||
from dm_env import specs
|
||||
from gym import core, spaces
|
||||
from gym import spaces
|
||||
from gym.core import ObsType
|
||||
|
||||
|
||||
def _spec_to_box(spec):
|
||||
def extract_min_max(s):
|
||||
assert s.dtype == np.float64 or s.dtype == np.float32, f"Only float64 and float32 types are allowed, instead {s.dtype} was found"
|
||||
assert s.dtype == np.float64 or s.dtype == np.float32, \
|
||||
f"Only float64 and float32 types are allowed, instead {s.dtype} was found"
|
||||
dim = int(np.prod(s.shape))
|
||||
if type(s) == specs.Array:
|
||||
bound = np.inf * np.ones(dim, dtype=s.dtype)
|
||||
@@ -32,7 +37,7 @@ def _spec_to_box(spec):
|
||||
return spaces.Box(low, high, dtype=s.dtype)
|
||||
|
||||
|
||||
def _flatten_obs(obs: collections.MutableMapping):
|
||||
def _flatten_obs(obs: MutableMapping):
|
||||
"""
|
||||
Flattens an observation of type MutableMapping, e.g. a dict to a 1D array.
|
||||
Args:
|
||||
@@ -42,7 +47,7 @@ def _flatten_obs(obs: collections.MutableMapping):
|
||||
|
||||
"""
|
||||
|
||||
if not isinstance(obs, collections.MutableMapping):
|
||||
if not isinstance(obs, MutableMapping):
|
||||
raise ValueError(f'Requires dict-like observations structure. {type(obs)} found.')
|
||||
|
||||
# Keep key order consistent for non OrderedDicts
|
||||
@@ -52,47 +57,19 @@ def _flatten_obs(obs: collections.MutableMapping):
|
||||
return np.concatenate(obs_vals)
|
||||
|
||||
|
||||
class DMCWrapper(core.Env):
|
||||
def __init__(
|
||||
self,
|
||||
domain_name: str,
|
||||
task_name: str,
|
||||
task_kwargs: dict = {},
|
||||
visualize_reward: bool = True,
|
||||
from_pixels: bool = False,
|
||||
height: int = 84,
|
||||
width: int = 84,
|
||||
camera_id: int = 0,
|
||||
frame_skip: int = 1,
|
||||
environment_kwargs: dict = None,
|
||||
channels_first: bool = True
|
||||
):
|
||||
assert 'random' in task_kwargs, 'Please specify a seed for deterministic behavior.'
|
||||
self._from_pixels = from_pixels
|
||||
self._height = height
|
||||
self._width = width
|
||||
self._camera_id = camera_id
|
||||
self._frame_skip = frame_skip
|
||||
self._channels_first = channels_first
|
||||
class DMCWrapper(gym.Env):
|
||||
def __init__(self,
|
||||
env: Callable[[], Union[composer.Environment, control.Environment]],
|
||||
):
|
||||
|
||||
# create task
|
||||
if domain_name == "manipulation":
|
||||
assert not from_pixels and not task_name.endswith("_vision"), \
|
||||
"TODO: Vision interface for manipulation is different to suite and needs to be implemented"
|
||||
self._env = manipulation.load(environment_name=task_name, seed=task_kwargs['random'])
|
||||
else:
|
||||
self._env = suite.load(domain_name=domain_name, task_name=task_name, task_kwargs=task_kwargs,
|
||||
visualize_reward=visualize_reward, environment_kwargs=environment_kwargs)
|
||||
# TODO: Currently this is required to be a function because dmc does not allow to copy composers environments
|
||||
self._env = env()
|
||||
|
||||
# action and observation space
|
||||
self._action_space = _spec_to_box([self._env.action_spec()])
|
||||
self._observation_space = _spec_to_box(self._env.observation_spec().values())
|
||||
|
||||
self._last_state = None
|
||||
self.viewer = None
|
||||
|
||||
# set seed
|
||||
self.seed(seed=task_kwargs.get('random', 1))
|
||||
self._window = None
|
||||
|
||||
def __getattr__(self, item):
|
||||
"""Propagate only non-existent properties to wrapped env."""
|
||||
@@ -103,17 +80,7 @@ class DMCWrapper(core.Env):
|
||||
return getattr(self._env, item)
|
||||
|
||||
def _get_obs(self, time_step):
|
||||
if self._from_pixels:
|
||||
obs = self.render(
|
||||
mode="rgb_array",
|
||||
height=self._height,
|
||||
width=self._width,
|
||||
camera_id=self._camera_id
|
||||
)
|
||||
if self._channels_first:
|
||||
obs = obs.transpose(2, 0, 1).copy()
|
||||
else:
|
||||
obs = _flatten_obs(time_step.observation).astype(self.observation_space.dtype)
|
||||
obs = _flatten_obs(time_step.observation).astype(self.observation_space.dtype)
|
||||
return obs
|
||||
|
||||
@property
|
||||
@@ -126,20 +93,7 @@ class DMCWrapper(core.Env):
|
||||
|
||||
@property
|
||||
def dt(self):
|
||||
return self._env.control_timestep() * self._frame_skip
|
||||
|
||||
@property
|
||||
def base_step_limit(self):
|
||||
"""
|
||||
Returns: max_episode_steps of the underlying DMC env
|
||||
|
||||
"""
|
||||
# Accessing private attribute because DMC does not expose time_limit or step_limit.
|
||||
# Only the current time_step/time as well as the control_timestep can be accessed.
|
||||
try:
|
||||
return (self._env._step_limit + self._frame_skip - 1) // self._frame_skip
|
||||
except AttributeError as e:
|
||||
return self._env._time_limit / self.dt
|
||||
return self._env.control_timestep()
|
||||
|
||||
def seed(self, seed=None):
|
||||
self._action_space.seed(seed)
|
||||
@@ -147,56 +101,71 @@ class DMCWrapper(core.Env):
|
||||
|
||||
def step(self, action) -> Tuple[np.ndarray, float, bool, Dict[str, Any]]:
|
||||
assert self._action_space.contains(action)
|
||||
reward = 0
|
||||
extra = {'internal_state': self._env.physics.get_state().copy()}
|
||||
|
||||
for _ in range(self._frame_skip):
|
||||
time_step = self._env.step(action)
|
||||
reward += time_step.reward or 0.
|
||||
done = time_step.last()
|
||||
if done:
|
||||
break
|
||||
|
||||
self._last_state = _flatten_obs(time_step.observation)
|
||||
time_step = self._env.step(action)
|
||||
reward = time_step.reward or 0.
|
||||
done = time_step.last()
|
||||
obs = self._get_obs(time_step)
|
||||
extra['discount'] = time_step.discount
|
||||
|
||||
return obs, reward, done, extra
|
||||
|
||||
def reset(self) -> np.ndarray:
|
||||
def reset(self, *, seed: Optional[int] = None, return_info: bool = False,
|
||||
options: Optional[dict] = None, ) -> Union[ObsType, Tuple[ObsType, dict]]:
|
||||
time_step = self._env.reset()
|
||||
self._last_state = _flatten_obs(time_step.observation)
|
||||
obs = self._get_obs(time_step)
|
||||
return obs
|
||||
|
||||
def render(self, mode='rgb_array', height=None, width=None, camera_id=0):
|
||||
if self._last_state is None:
|
||||
raise ValueError('Environment not ready to render. Call reset() first.')
|
||||
|
||||
camera_id = camera_id or self._camera_id
|
||||
def render(self, mode='rgb_array', height=240, width=320, camera_id=-1, overlays=(), depth=False,
|
||||
segmentation=False, scene_option=None, render_flag_overrides=None):
|
||||
|
||||
# assert mode == 'rgb_array', 'only support rgb_array mode, given %s' % mode
|
||||
if mode == "rgb_array":
|
||||
height = height or self._height
|
||||
width = width or self._width
|
||||
return self._env.physics.render(height=height, width=width, camera_id=camera_id)
|
||||
return self._env.physics.render(height=height, width=width, camera_id=camera_id, overlays=overlays,
|
||||
depth=depth, segmentation=segmentation, scene_option=scene_option,
|
||||
render_flag_overrides=render_flag_overrides)
|
||||
|
||||
elif mode == 'human':
|
||||
if self.viewer is None:
|
||||
# pylint: disable=import-outside-toplevel
|
||||
# pylint: disable=g-import-not-at-top
|
||||
from gym.envs.classic_control import rendering
|
||||
self.viewer = rendering.SimpleImageViewer()
|
||||
# Render max available buffer size. Larger is only possible by altering the XML.
|
||||
img = self._env.physics.render(height=self._env.physics.model.vis.global_.offheight,
|
||||
width=self._env.physics.model.vis.global_.offwidth,
|
||||
camera_id=camera_id)
|
||||
self.viewer.imshow(img)
|
||||
return self.viewer.isopen
|
||||
# Render max available buffer size. Larger is only possible by altering the XML.
|
||||
img = self._env.physics.render(height=self._env.physics.model.vis.global_.offheight,
|
||||
width=self._env.physics.model.vis.global_.offwidth,
|
||||
camera_id=camera_id, overlays=overlays, depth=depth, segmentation=segmentation,
|
||||
scene_option=scene_option, render_flag_overrides=render_flag_overrides)
|
||||
|
||||
if depth:
|
||||
img = np.dstack([img.astype(np.uint8)] * 3)
|
||||
|
||||
if mode == 'human':
|
||||
try:
|
||||
import cv2
|
||||
if self._window is None:
|
||||
self._window = cv2.namedWindow(self.id, cv2.WINDOW_AUTOSIZE)
|
||||
|
||||
cv2.imshow(self.id, img[..., ::-1]) # Image in BGR
|
||||
cv2.waitKey(1)
|
||||
except ImportError:
|
||||
import pygame
|
||||
img = img.transpose((1, 0, 2))
|
||||
if self._window is None:
|
||||
pygame.init()
|
||||
pygame.display.init()
|
||||
self._window = pygame.display.set_mode(img.shape[:2])
|
||||
|
||||
self._window.blit(pygame.surfarray.make_surface(img), (0, 0))
|
||||
pygame.event.pump()
|
||||
pygame.display.flip()
|
||||
|
||||
def close(self):
|
||||
super().close()
|
||||
if self.viewer is not None and self.viewer.isopen:
|
||||
self.viewer.close()
|
||||
if self._window is not None:
|
||||
try:
|
||||
import cv2
|
||||
cv2.destroyWindow(self.id)
|
||||
except ImportError:
|
||||
import pygame
|
||||
|
||||
pygame.display.quit()
|
||||
pygame.quit()
|
||||
|
||||
@property
|
||||
def reward_range(self) -> Tuple[float, float]:
|
||||
@@ -204,3 +173,8 @@ class DMCWrapper(core.Env):
|
||||
if isinstance(reward_spec, specs.BoundedArray):
|
||||
return reward_spec.minimum, reward_spec.maximum
|
||||
return -float('inf'), float('inf')
|
||||
|
||||
@property
|
||||
def metadata(self):
|
||||
return {'render.modes': ['human', 'rgb_array'],
|
||||
'video.frames_per_second': round(1.0 / self._env.control_timestep())}
|
||||
|
||||
Reference in New Issue
Block a user