zhiweiyoung
commited on
Commit
•
ec2df60
1
Parent(s):
1ffdf2c
larger batch size
Browse files- README.md +1 -1
- config.json +1 -1
- replay.mp4 +0 -0
- results.json +1 -1
- zhiwei_ppo_m.zip +3 -0
- zhiwei_ppo_m/_stable_baselines3_version +1 -0
- zhiwei_ppo_m/data +115 -0
- zhiwei_ppo_m/policy.optimizer.pth +3 -0
- zhiwei_ppo_m/policy.pth +3 -0
- zhiwei_ppo_m/pytorch_variables.pth +3 -0
- zhiwei_ppo_m/system_info.txt +8 -0
README.md
CHANGED
@@ -16,7 +16,7 @@ model-index:
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
-
value:
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
|
|
16 |
type: LunarLander-v2
|
17 |
metrics:
|
18 |
- type: mean_reward
|
19 |
+
value: 286.90 +/- 21.83
|
20 |
name: mean_reward
|
21 |
verified: false
|
22 |
---
|
config.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function ActorCriticPolicy.__init__ at 0x000001EFDA765D30>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001EFDA765DC0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001EFDA765E50>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001EFDA765EE0>", "_build": "<function ActorCriticPolicy._build at 0x000001EFDA765F70>", "forward": "<function ActorCriticPolicy.forward at 0x000001EFDA767040>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x000001EFDA7670D0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001EFDA767160>", "_predict": "<function ActorCriticPolicy._predict at 0x000001EFDA7671F0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001EFDA767280>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001EFDA767310>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001EFDA7673A0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x000001EFDA7545C0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 4063232, "_total_timesteps": 4000000, "_num_timesteps_at_start": 0, "seed": 42, "action_noise": null, "start_time": 1704541431390488100, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 744, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-1.5 -1.5 -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[1.5 1.5 5. 5. 3.1415927 5. 1.\n 1. ]", "low_repr": "[-1.5 -1.5 -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[1.5 1.5 5. 5. 3.1415927 5. 1.\n 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWVoAEAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlIwUbnVtcHkucmFuZG9tLl9waWNrbGWUjBBfX2dlbmVyYXRvcl9jdG9ylJOUjAVQQ0c2NJRoG4wUX19iaXRfZ2VuZXJhdG9yX2N0b3KUk5SGlFKUfZQojA1iaXRfZ2VuZXJhdG9ylIwFUENHNjSUjAVzdGF0ZZR9lChoJooRyAqGaJ18vKwqj3mYZ0+kzgCMA2luY5SKEf+rJY3i8spmbkGoyTZUUPoAdYwKaGFzX3VpbnQzMpRLAIwIdWludGVnZXKUSwB1YnViLg==", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 32, "n_steps": 2048, "gamma": 0.999, "gae_lambda": 0.95, "ent_coef": 0.02, "vf_coef": 0.5, "max_grad_norm": 0.5, "rollout_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNgAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwNUm9sbG91dEJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'advantages': <class 'numpy.ndarray'>, 'returns': <class 'numpy.ndarray'>, 'episode_starts': <class 'numpy.ndarray'>, 'log_probs': <class 'numpy.ndarray'>, 'values': <class 'numpy.ndarray'>}", "__doc__": "\n Rollout buffer used in on-policy algorithms like A2C/PPO.\n It corresponds to ``buffer_size`` transitions collected\n using the current policy.\n This experience will be discarded after the policy update.\n In order to use PPO objective, we also store the current value of each state\n and the log probability of each taken action.\n\n The term rollout here refers to the model-free notion and should not\n be used with the concept of rollout used in model-based RL or planning.\n Hence, it is only involved in policy and value function training but not action selection.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n Equivalent to classic advantage when set to 1.\n :param gamma: Discount factor\n :param n_envs: Number of parallel environments\n ", "__init__": "<function RolloutBuffer.__init__ at 0x000001EFDA47AAF0>", "reset": "<function RolloutBuffer.reset at 0x000001EFDA47AB80>", "compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x000001EFDA47AC10>", "add": "<function RolloutBuffer.add at 0x000001EFDA47ACA0>", "get": "<function RolloutBuffer.get at 0x000001EFDA47AD30>", "_get_samples": "<function RolloutBuffer._get_samples at 0x000001EFDA47ADC0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x000001EFDA47D740>"}, "rollout_buffer_kwargs": {}, "batch_size": 128, "n_epochs": 6, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Windows-10-10.0.22621-SP0 10.0.22621", "Python": "3.9.18", "Stable-Baselines3": "2.2.1", "PyTorch": "2.1.2+cu121", "GPU Enabled": "True", "Numpy": "1.26.2", "Cloudpickle": "3.0.0", "Gymnasium": "0.29.1"}}
|
|
|
1 |
+
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__module__": "stable_baselines3.common.policies", "__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ", "__init__": "<function ActorCriticPolicy.__init__ at 0x0000026BADF0CCA0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x0000026BADF0CD30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x0000026BADF0CDC0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x0000026BADF0CE50>", "_build": "<function ActorCriticPolicy._build at 0x0000026BADF0CEE0>", "forward": "<function ActorCriticPolicy.forward at 0x0000026BADF0CF70>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x0000026BADF0E040>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x0000026BADF0E0D0>", "_predict": "<function ActorCriticPolicy._predict at 0x0000026BADF0E160>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x0000026BADF0E1F0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x0000026BADF0E280>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x0000026BADF0E310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x0000026BABB2D080>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 4063232, "_total_timesteps": 4000000, "_num_timesteps_at_start": 0, "seed": 42, "action_noise": null, "start_time": 1704567954825889800, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 1059, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-1.5 -1.5 -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[1.5 1.5 5. 5. 3.1415927 5. 1.\n 1. ]", "low_repr": "[-1.5 -1.5 -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[1.5 1.5 5. 5. 3.1415927 5. 1.\n 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "n_envs": 32, "n_steps": 4096, "gamma": 0.9999, "gae_lambda": 0.91, "ent_coef": 0.02, "vf_coef": 0.5, "max_grad_norm": 0.5, "rollout_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNgAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwNUm9sbG91dEJ1ZmZlcpSTlC4=", "__module__": "stable_baselines3.common.buffers", "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'advantages': <class 'numpy.ndarray'>, 'returns': <class 'numpy.ndarray'>, 'episode_starts': <class 'numpy.ndarray'>, 'log_probs': <class 'numpy.ndarray'>, 'values': <class 'numpy.ndarray'>}", "__doc__": "\n Rollout buffer used in on-policy algorithms like A2C/PPO.\n It corresponds to ``buffer_size`` transitions collected\n using the current policy.\n This experience will be discarded after the policy update.\n In order to use PPO objective, we also store the current value of each state\n and the log probability of each taken action.\n\n The term rollout here refers to the model-free notion and should not\n be used with the concept of rollout used in model-based RL or planning.\n Hence, it is only involved in policy and value function training but not action selection.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n Equivalent to classic advantage when set to 1.\n :param gamma: Discount factor\n :param n_envs: Number of parallel environments\n ", "__init__": "<function RolloutBuffer.__init__ at 0x0000026BADC22A60>", "reset": "<function RolloutBuffer.reset at 0x0000026BADC22AF0>", "compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x0000026BADC22B80>", "add": "<function RolloutBuffer.add at 0x0000026BADC22C10>", "get": "<function RolloutBuffer.get at 0x0000026BADC22CA0>", "_get_samples": "<function RolloutBuffer._get_samples at 0x0000026BADC22D30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x0000026BADC260C0>"}, "rollout_buffer_kwargs": {}, "batch_size": 128, "n_epochs": 6, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Windows-10-10.0.22621-SP0 10.0.22621", "Python": "3.9.18", "Stable-Baselines3": "2.2.1", "PyTorch": "2.1.2+cu121", "GPU Enabled": "True", "Numpy": "1.26.2", "Cloudpickle": "3.0.0", "Gymnasium": "0.29.1"}}
|
replay.mp4
CHANGED
Binary files a/replay.mp4 and b/replay.mp4 differ
|
|
results.json
CHANGED
@@ -1 +1 @@
|
|
1 |
-
{"mean_reward":
|
|
|
1 |
+
{"mean_reward": 286.8974239, "std_reward": 21.825747025202496, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-01-07T09:21:34.385320"}
|
zhiwei_ppo_m.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:dcd17d55c3d9e536f696d4d1eff3b452192016b3a53009af98258569bcf29e60
|
3 |
+
size 150940
|
zhiwei_ppo_m/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
2.2.1
|
zhiwei_ppo_m/data
ADDED
@@ -0,0 +1,115 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
|
5 |
+
"__module__": "stable_baselines3.common.policies",
|
6 |
+
"__doc__": "\n Policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space\n :param action_space: Action space\n :param lr_schedule: Learning rate schedule (could be constant)\n :param net_arch: The specification of the policy and value networks.\n :param activation_fn: Activation function\n :param ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Features extractor to use.\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\n :param normalize_images: Whether to normalize images or not,\n dividing by 255.0 (True by default)\n :param optimizer_class: The optimizer to use,\n ``th.optim.Adam`` by default\n :param optimizer_kwargs: Additional keyword arguments,\n excluding the learning rate, to pass to the optimizer\n ",
|
7 |
+
"__init__": "<function ActorCriticPolicy.__init__ at 0x0000026BADF0CCA0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x0000026BADF0CD30>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x0000026BADF0CDC0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x0000026BADF0CE50>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x0000026BADF0CEE0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x0000026BADF0CF70>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x0000026BADF0E040>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x0000026BADF0E0D0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x0000026BADF0E160>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x0000026BADF0E1F0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x0000026BADF0E280>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x0000026BADF0E310>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x0000026BABB2D080>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"num_timesteps": 4063232,
|
25 |
+
"_total_timesteps": 4000000,
|
26 |
+
"_num_timesteps_at_start": 0,
|
27 |
+
"seed": 42,
|
28 |
+
"action_noise": null,
|
29 |
+
"start_time": 1704567954825889800,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": null,
|
32 |
+
"_last_obs": {
|
33 |
+
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "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"
|
35 |
+
},
|
36 |
+
"_last_episode_starts": {
|
37 |
+
":type:": "<class 'numpy.ndarray'>",
|
38 |
+
":serialized:": "gAWVkwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksghZSMAUOUdJRSlC4="
|
39 |
+
},
|
40 |
+
"_last_original_obs": null,
|
41 |
+
"_episode_num": 0,
|
42 |
+
"use_sde": false,
|
43 |
+
"sde_sample_freq": -1,
|
44 |
+
"_current_progress_remaining": -0.015808000000000044,
|
45 |
+
"_stats_window_size": 100,
|
46 |
+
"ep_info_buffer": {
|
47 |
+
":type:": "<class 'collections.deque'>",
|
48 |
+
":serialized:": "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"
|
49 |
+
},
|
50 |
+
"ep_success_buffer": {
|
51 |
+
":type:": "<class 'collections.deque'>",
|
52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
53 |
+
},
|
54 |
+
"_n_updates": 1059,
|
55 |
+
"observation_space": {
|
56 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
57 |
+
":serialized:": "gAWVZgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAADAvwAAwL8AAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAwD8AAMA/AACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFNbLTEuNSAgICAgICAtMS41ICAgICAgIC01LiAgICAgICAgLTUuICAgICAgICAtMy4xNDE1OTI3IC01LgogLTAuICAgICAgICAtMC4gICAgICAgXZSMCWhpZ2hfcmVwcpSMS1sxLjUgICAgICAgMS41ICAgICAgIDUuICAgICAgICA1LiAgICAgICAgMy4xNDE1OTI3IDUuICAgICAgICAxLgogMS4gICAgICAgXZSMCl9ucF9yYW5kb22UTnViLg==",
|
58 |
+
"dtype": "float32",
|
59 |
+
"bounded_below": "[ True True True True True True True True]",
|
60 |
+
"bounded_above": "[ True True True True True True True True]",
|
61 |
+
"_shape": [
|
62 |
+
8
|
63 |
+
],
|
64 |
+
"low": "[-1.5 -1.5 -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
65 |
+
"high": "[1.5 1.5 5. 5. 3.1415927 5. 1.\n 1. ]",
|
66 |
+
"low_repr": "[-1.5 -1.5 -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
67 |
+
"high_repr": "[1.5 1.5 5. 5. 3.1415927 5. 1.\n 1. ]",
|
68 |
+
"_np_random": null
|
69 |
+
},
|
70 |
+
"action_space": {
|
71 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
72 |
+
":serialized:": "gAWVoAEAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlIwUbnVtcHkucmFuZG9tLl9waWNrbGWUjBBfX2dlbmVyYXRvcl9jdG9ylJOUjAVQQ0c2NJRoG4wUX19iaXRfZ2VuZXJhdG9yX2N0b3KUk5SGlFKUfZQojA1iaXRfZ2VuZXJhdG9ylIwFUENHNjSUjAVzdGF0ZZR9lChoJooRyAqGaJ18vKwqj3mYZ0+kzgCMA2luY5SKEf+rJY3i8spmbkGoyTZUUPoAdYwKaGFzX3VpbnQzMpRLAIwIdWludGVnZXKUSwB1YnViLg==",
|
73 |
+
"n": "4",
|
74 |
+
"start": "0",
|
75 |
+
"_shape": [],
|
76 |
+
"dtype": "int64",
|
77 |
+
"_np_random": "Generator(PCG64)"
|
78 |
+
},
|
79 |
+
"n_envs": 32,
|
80 |
+
"n_steps": 4096,
|
81 |
+
"gamma": 0.9999,
|
82 |
+
"gae_lambda": 0.91,
|
83 |
+
"ent_coef": 0.02,
|
84 |
+
"vf_coef": 0.5,
|
85 |
+
"max_grad_norm": 0.5,
|
86 |
+
"rollout_buffer_class": {
|
87 |
+
":type:": "<class 'abc.ABCMeta'>",
|
88 |
+
":serialized:": "gAWVNgAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwNUm9sbG91dEJ1ZmZlcpSTlC4=",
|
89 |
+
"__module__": "stable_baselines3.common.buffers",
|
90 |
+
"__annotations__": "{'observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'advantages': <class 'numpy.ndarray'>, 'returns': <class 'numpy.ndarray'>, 'episode_starts': <class 'numpy.ndarray'>, 'log_probs': <class 'numpy.ndarray'>, 'values': <class 'numpy.ndarray'>}",
|
91 |
+
"__doc__": "\n Rollout buffer used in on-policy algorithms like A2C/PPO.\n It corresponds to ``buffer_size`` transitions collected\n using the current policy.\n This experience will be discarded after the policy update.\n In order to use PPO objective, we also store the current value of each state\n and the log probability of each taken action.\n\n The term rollout here refers to the model-free notion and should not\n be used with the concept of rollout used in model-based RL or planning.\n Hence, it is only involved in policy and value function training but not action selection.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator\n Equivalent to classic advantage when set to 1.\n :param gamma: Discount factor\n :param n_envs: Number of parallel environments\n ",
|
92 |
+
"__init__": "<function RolloutBuffer.__init__ at 0x0000026BADC22A60>",
|
93 |
+
"reset": "<function RolloutBuffer.reset at 0x0000026BADC22AF0>",
|
94 |
+
"compute_returns_and_advantage": "<function RolloutBuffer.compute_returns_and_advantage at 0x0000026BADC22B80>",
|
95 |
+
"add": "<function RolloutBuffer.add at 0x0000026BADC22C10>",
|
96 |
+
"get": "<function RolloutBuffer.get at 0x0000026BADC22CA0>",
|
97 |
+
"_get_samples": "<function RolloutBuffer._get_samples at 0x0000026BADC22D30>",
|
98 |
+
"__abstractmethods__": "frozenset()",
|
99 |
+
"_abc_impl": "<_abc._abc_data object at 0x0000026BADC260C0>"
|
100 |
+
},
|
101 |
+
"rollout_buffer_kwargs": {},
|
102 |
+
"batch_size": 128,
|
103 |
+
"n_epochs": 6,
|
104 |
+
"clip_range": {
|
105 |
+
":type:": "<class 'function'>",
|
106 |
+
":serialized:": "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"
|
107 |
+
},
|
108 |
+
"clip_range_vf": null,
|
109 |
+
"normalize_advantage": true,
|
110 |
+
"target_kl": null,
|
111 |
+
"lr_schedule": {
|
112 |
+
":type:": "<class 'function'>",
|
113 |
+
":serialized:": "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"
|
114 |
+
}
|
115 |
+
}
|
zhiwei_ppo_m/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:97ab525d704367e0eb9882e7da5700766089734f08c8cb04432b2e325d5416ab
|
3 |
+
size 88362
|
zhiwei_ppo_m/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:55cce5a0751be1942a3063c0833441054db8b0038d8d0370dfafec557ef8766c
|
3 |
+
size 43762
|
zhiwei_ppo_m/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fb4dde0c1ad63b7740276006a06cc491b21b407ea6c889928c223ec77ddad79f
|
3 |
+
size 864
|
zhiwei_ppo_m/system_info.txt
ADDED
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Windows-10-10.0.22621-SP0 10.0.22621
|
2 |
+
- Python: 3.9.18
|
3 |
+
- Stable-Baselines3: 2.2.1
|
4 |
+
- PyTorch: 2.1.2+cu121
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.26.2
|
7 |
+
- Cloudpickle: 3.0.0
|
8 |
+
- Gymnasium: 0.29.1
|