Upload PPO LunarLander-v2 trained agent

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: 274.85 +/- 15.93
+      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 0x7d74a1045d80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d74a1045e10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d74a1045ea0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d74a1045f30>", "_build": "<function ActorCriticPolicy._build at 0x7d74a1045fc0>", "forward": "<function ActorCriticPolicy.forward at 0x7d74a1046050>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d74a10460e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d74a1046170>", "_predict": "<function ActorCriticPolicy._predict at 0x7d74a1046200>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d74a1046290>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d74a1046320>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d74a10463b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d74a11ebd00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 4849664, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1729953818446581496, "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": 0.5150336, "_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": 590, "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": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "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-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": "2.5.0+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "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:aa2d96866f11a7b37d8f00d854c93be254887c679ac8663daf9f9ab8cb6a7745
+size 147878

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 0x7d74a1045d80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d74a1045e10>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d74a1045ea0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d74a1045f30>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d74a1045fc0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d74a1046050>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d74a10460e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d74a1046170>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d74a1046200>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d74a1046290>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d74a1046320>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d74a10463b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d74a11ebd00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 4849664,
+    "_total_timesteps": 10000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1729953818446581496,
+    "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": 0.5150336,
+    "_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": 590,
+    "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": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 128,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0f3c80825c0eaeaae160a0a5c9b7048109572c3ab84ae636f4ac486074e78701
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c5b550d1b7b52d72d7c09f062e71928f3f59ccb39e42224de291a27696c86756
+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: 2.5.0+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.0
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 274.8542460658232, "std_reward": 15.925440893588176, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-10-26T15:43:27.707721"}