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: 286.52 +/- 24.32
+      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 0x000001B97DE86B80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001B97DE86C10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001B97DE86CA0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001B97DE86D30>", "_build": "<function ActorCriticPolicy._build at 0x000001B97DE86DC0>", "forward": "<function ActorCriticPolicy.forward at 0x000001B97DE86E50>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x000001B97DE86EE0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001B97DE86F70>", "_predict": "<function ActorCriticPolicy._predict at 0x000001B97DE8C040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001B97DE8C0D0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001B97DE8C160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001B97DE8C1F0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x000001B97D8D5140>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 64, "num_timesteps": 5046272, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1679964996914067000, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.009254400000000107, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 308, "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, "system_info": {"OS": "Windows-10-10.0.22621-SP0 10.0.22621", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.24.2", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ba7e3e404075ee4e4cfa2996ce38003c0ffee7cfb9629dcada640100a8c99a86
+size 149422

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x000001B97DE86B80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001B97DE86C10>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001B97DE86CA0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001B97DE86D30>",
+        "_build": "<function ActorCriticPolicy._build at 0x000001B97DE86DC0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x000001B97DE86E50>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x000001B97DE86EE0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001B97DE86F70>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x000001B97DE8C040>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001B97DE8C0D0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001B97DE8C160>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001B97DE8C1F0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x000001B97D8D5140>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 64,
+    "num_timesteps": 5046272,
+    "_total_timesteps": 5000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1679964996914067000,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "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"
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVswAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJZAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiS0CFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.009254400000000107,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 308,
+    "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:": "gAWVkgIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMYEM6XFVzZXJzXG1hcmszXG1pbmljb25kYTNcZW52c1xodWdnaW5nZmFjZS0xXGxpYlxzaXRlLXBhY2thZ2VzXHN0YWJsZV9iYXNlbGluZXMzXGNvbW1vblx1dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5RoDHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB59lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bbda43a9bfe5cdfbd57c5fad1fd54c8c08fedddffe88a4a64ebbaa9cf49c8691
+size 88057

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4bb49f2e92a7aa56439326f74f988a624d6cc047e4181c0c63642102d23dcb3d
+size 43393

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 +1,7 @@
+- OS: Windows-10-10.0.22621-SP0 10.0.22621
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 2.0.0+cu118
+- GPU Enabled: True
+- Numpy: 1.24.2
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 286.52228732259084, "std_reward": 24.31792044356092, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-28T05:59:32.433201"}