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: 256.33 +/- 18.13
+      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 0x7b0fa36f6830>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b0fa36f68c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b0fa36f6950>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b0fa36f69e0>", "_build": "<function ActorCriticPolicy._build at 0x7b0fa36f6a70>", "forward": "<function ActorCriticPolicy.forward at 0x7b0fa36f6b00>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b0fa36f6b90>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b0fa36f6c20>", "_predict": "<function ActorCriticPolicy._predict at 0x7b0fa36f6cb0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b0fa36f6d40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b0fa36f6dd0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b0fa36f6e60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b0fa3676680>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 4153344, "_total_timesteps": 10000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1736279717638620493, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWV9QAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaAAAAAAAAAAJpx3rtcW266qD3AM+cEE6/JJle7os6iswAAgD8AAIA/gI+pvX+uAz641mM9fK2Bvm7Srzyh/Y88AAAAAAAAAAAmGVk+vS5uPAV85Tmy1yE454YAPj6aErkAAIA/AACAP5r/AD8Ur8M+QwKsO3Dwa75ZIxc+keZBvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.5846656, "_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": 5063, "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": 4, "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": "2.5.1+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:d2c888bcd871b99501372fd0b66c4f4316c2bf8b2d432ade0d6e2b9ad83c30a5
+size 147476

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 0x7b0fa36f6830>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b0fa36f68c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b0fa36f6950>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b0fa36f69e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b0fa36f6a70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b0fa36f6b00>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b0fa36f6b90>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b0fa36f6c20>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b0fa36f6cb0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b0fa36f6d40>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b0fa36f6dd0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b0fa36f6e60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b0fa3676680>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 4153344,
+    "_total_timesteps": 10000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1736279717638620493,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWV9QAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaAAAAAAAAAAJpx3rtcW266qD3AM+cEE6/JJle7os6iswAAgD8AAIA/gI+pvX+uAz641mM9fK2Bvm7Srzyh/Y88AAAAAAAAAAAmGVk+vS5uPAV85Tmy1yE454YAPj6aErkAAIA/AACAP5r/AD8Ur8M+QwKsO3Dwa75ZIxc+keZBvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksESwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 0.5846656,
+    "_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": 5063,
+    "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": 4,
+    "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 sha256:4a0dd5634c6a7e07d7844df820eddc06fd4d44d2af4974d03881617a3dfb7138
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d810bfa885364e6abb703ac04d923242c0dc66758e51d7fc872e0797382dac0e
+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: 2.5.1+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": 256.333419, "std_reward": 18.132942250566998, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-01-07T21:46:52.810419"}