Upload LunarLander 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: 248.15 +/- 34.91
+      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 0x78671280f640>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78671280f6d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78671280f760>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78671280f7f0>", "_build": "<function ActorCriticPolicy._build at 0x78671280f880>", "forward": "<function ActorCriticPolicy.forward at 0x78671280f910>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78671280f9a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78671280fa30>", "_predict": "<function ActorCriticPolicy._predict at 0x78671280fac0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78671280fb50>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78671280fbe0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78671280fc70>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7866b66d5e00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1690928352488268896, "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": 310, "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": 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-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo_test.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14f66ec881dfdbcedbce6b84cd3663066180fce9268d0efc61050d13eaefff04
+size 146689

ppo_test/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo_test/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 0x78671280f640>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78671280f6d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78671280f760>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78671280f7f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x78671280f880>",
+        "forward": "<function ActorCriticPolicy.forward at 0x78671280f910>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78671280f9a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78671280fa30>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x78671280fac0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78671280fb50>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78671280fbe0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78671280fc70>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7866b66d5e00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1690928352488268896,
+    "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": 310,
+    "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": 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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo_test/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:64ea0ac375a2eb99747f6ef710112405ee3ee2b3ec27ad083156386d17bff018
+size 87929

ppo_test/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:29a40ccc2bcb42aace114b7af9ad749d0c31d86561697d022f976e3d8a5e62df
+size 43329

ppo_test/pytorch_variables.pth

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

ppo_test/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-5.15.109+-x86_64-with-glibc2.35 # 1 SMP Fri Jun 9 10:57:30 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 248.1482974, "std_reward": 34.907622441049305, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-01T22:57:56.796522"}