init

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 257.74 +/- 18.38
+      name: mean_reward
+      verified: false
+---
+
+# **PPO** Agent playing **LunarLander-v2**
+This is a trained model of a **PPO** agent playing **LunarLander-v2**
+using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+
+## Usage (with Stable-baselines3)
+TODO: Add your code
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +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 0x7f42c0a8ac20>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f42c0a8acb0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f42c0a8ad40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f42c0a8add0>", "_build": "<function ActorCriticPolicy._build at 0x7f42c0a8ae60>", "forward": "<function ActorCriticPolicy.forward at 0x7f42c0a8aef0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f42c0a8af80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f42c0a8b010>", "_predict": "<function ActorCriticPolicy._predict at 0x7f42c0a8b0a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f42c0a8b130>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f42c0a8b1c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f42c0a8b250>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f42c0a86640>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1687537466447716553, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_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": 248, "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": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "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": "Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 257.7440728, "std_reward": 18.377927384688878, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-06-23T16:52:23.308320"}

shuheng.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c89f0862b8c7e9b4acf9a5812d49056eaf2f66903def61a78d2b9be6020f9e0e
+size 146755

shuheng/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

shuheng/data

@@ -0,0 +1,99 @@
+{
+    "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 0x7f42c0a8ac20>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f42c0a8acb0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f42c0a8ad40>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f42c0a8add0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f42c0a8ae60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f42c0a8aef0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f42c0a8af80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f42c0a8b010>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f42c0a8b0a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f42c0a8b130>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f42c0a8b1c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f42c0a8b250>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f42c0a86640>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1687537466447716553,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
+    },
+    "_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": 248,
+    "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": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "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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"
+    }
+}

shuheng/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a51a1a132ae6eccbd906f7aa778f89c42c9cf5d5049a77895bf22219abd7e94
+size 87929

shuheng/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3d239c7a2aa40d592ed53d9b4f64f5a4f68b471b5603f2816cc36db459b21c3b
+size 43329

shuheng/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

shuheng/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2