My 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: 266.83 +/- 24.97
+      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 0x793e4243b640>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x793e4243b6d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x793e4243b760>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x793e4243b7f0>", "_build": "<function ActorCriticPolicy._build at 0x793e4243b880>", "forward": "<function ActorCriticPolicy.forward at 0x793e4243b910>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x793e4243b9a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x793e4243ba30>", "_predict": "<function ActorCriticPolicy._predict at 0x793e4243bac0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x793e4243bb50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x793e4243bbe0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x793e4243bc70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x793e425cd280>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1711978714353250098, "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": 248, "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:a12560e26560a6bfed42cf010b5f0093a5c9cc4f478940e67dd7975586826115
+size 148076

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 0x793e4243b640>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x793e4243b6d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x793e4243b760>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x793e4243b7f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x793e4243b880>",
+        "forward": "<function ActorCriticPolicy.forward at 0x793e4243b910>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x793e4243b9a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x793e4243ba30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x793e4243bac0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x793e4243bb50>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x793e4243bbe0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x793e4243bc70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x793e425cd280>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1711978714353250098,
+    "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": 248,
+    "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:cfcf1dfea49a84403af4c0e320f0a7f471aff3fd73a2cabe99bcc4680a638369
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:38ff521eddb7586287bb15ed76045e75613fa3e2fb691f351f9914ee17238f31
+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": 266.8349299, "std_reward": 24.96888034547458, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-01T14:00:07.672322"}