First RL-MODEL

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: 274.32 +/- 18.37
+      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 0x7ca0f5f3cb80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ca0f5f3cc10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ca0f5f3cca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ca0f5f3cd30>", "_build": "<function ActorCriticPolicy._build at 0x7ca0f5f3cdc0>", "forward": "<function ActorCriticPolicy.forward at 0x7ca0f5f3ce50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ca0f5f3cee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ca0f5f3cf70>", "_predict": "<function ActorCriticPolicy._predict at 0x7ca0f5f3d000>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ca0f5f3d090>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ca0f5f3d120>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ca0f5f3d1b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ca0f5ede240>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 5013504, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713180591522445112, "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": -0.0027007999999999477, "_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": 1252, "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": 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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+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:b9be5fae86838f91cb1a2221a187c80458e61faccb7531b78eaabaa3ddfdc950
+size 147962

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 0x7ca0f5f3cb80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ca0f5f3cc10>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ca0f5f3cca0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ca0f5f3cd30>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ca0f5f3cdc0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ca0f5f3ce50>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ca0f5f3cee0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ca0f5f3cf70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ca0f5f3d000>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ca0f5f3d090>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ca0f5f3d120>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ca0f5f3d1b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ca0f5ede240>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 5013504,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1713180591522445112,
+    "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": -0.0027007999999999477,
+    "_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": 1252,
+    "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": 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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e751900da5435a514ecf11f96417267146be488786cf81b1d9918adecaec0f37
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38f3bb7da2e8978abe370e9b7378d350da72f3bab152b6f16155ecdd69337e4d
+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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+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": 274.3200095, "std_reward": 18.370231808813173, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-15T12:40:35.762640"}