My first Reinforcement Learning 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: 256.48 +/- 22.26
+      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 0x7d5fe2292b00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d5fe2292b90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d5fe2292c20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d5fe2292cb0>", "_build": "<function ActorCriticPolicy._build at 0x7d5fe2292d40>", "forward": "<function ActorCriticPolicy.forward at 0x7d5fe2292dd0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d5fe2292e60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d5fe2292ef0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d5fe2292f80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d5fe2293010>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d5fe22930a0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d5fe2293130>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d5fe243ec80>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 901120, "_total_timesteps": 900000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1695355960223999077, "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": -0.0012444444444443814, "_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": 220, "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.23.5", "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 sha256:04f8c101a5f7c7045b233831b9306870697daedc45877f632324c6c75cddfab2
+size 146750

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 0x7d5fe2292b00>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d5fe2292b90>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d5fe2292c20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d5fe2292cb0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d5fe2292d40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d5fe2292dd0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d5fe2292e60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d5fe2292ef0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d5fe2292f80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d5fe2293010>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d5fe22930a0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d5fe2293130>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d5fe243ec80>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 901120,
+    "_total_timesteps": 900000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1695355960223999077,
+    "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": -0.0012444444444443814,
+    "_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": 220,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4b0171b87631769d6573afca649d062c884bd35a793d07206efbbcbd1f834f30
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6463c32ca6afd6fb2cee8f1fb0cc7db214625717577b4762811f810ca2b8299f
+size 43329

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 256.4775367000001, "std_reward": 22.261815216361477, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-22T04:42:30.167581"}