colab-v1.0

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: 249.93 +/- 36.25
+      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 0x7af146f72a70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7af146f72b00>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7af146f72b90>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7af146f72c20>", "_build": "<function ActorCriticPolicy._build at 0x7af146f72cb0>", "forward": "<function ActorCriticPolicy.forward at 0x7af146f72d40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7af146f72dd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7af146f72e60>", "_predict": "<function ActorCriticPolicy._predict at 0x7af146f72ef0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7af146f72f80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7af146f73010>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7af146f730a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7af146f19200>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1720261334785080457, "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": 310, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2424fba9b886e0c493007e75be3dfe028a5b23d5022f77a37a44b028124371e4
+size 148015

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7af146f72a70>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7af146f72b00>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7af146f72b90>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7af146f72c20>",
+        "_build": "<function ActorCriticPolicy._build at 0x7af146f72cb0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7af146f72d40>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7af146f72dd0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7af146f72e60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7af146f72ef0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7af146f72f80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7af146f73010>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7af146f730a0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7af146f19200>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1720261334785080457,
+    "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": 310,
+    "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "n_steps": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fc4a22c23a4c5ecaa3cf05dfe00283e5a1dde93f23a6e3b5bb5c7b3390613f66
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8a2dd7be7be41834b410164b69ecfc95ac3f8bf9a3d0050b81647eabb5495175
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.3.0+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 249.9348644, "std_reward": 36.25458349721542, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-07-06T11:07:53.404117"}