Upload PPO LunarLander-v2 trained agent

Lunar-Lander-v1.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d7cbcad54752198530ad1ef7d1b57e63f20b0cec70320a600b131ae1b35fce8
+size 146746

Lunar-Lander-v1/_stable_baselines3_version

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

Lunar-Lander-v1/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 0x7c2fd0efd900>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c2fd0efd990>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c2fd0efda20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c2fd0efdab0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7c2fd0efdb40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7c2fd0efdbd0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c2fd0efdc60>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c2fd0efdcf0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7c2fd0efdd80>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c2fd0efde10>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c2fd0efdea0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c2fd0efdf30>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7c2fd0ef8fc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1691856922484089188,
+    "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:": "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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"
+    }
+}

Lunar-Lander-v1/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83d299d487d244898f4ff4b1f46efadf04a487381904a038fb018ccfa8d19404
+size 87929

Lunar-Lander-v1/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a29ca69be0c61483555dec6d4416ba8627c15526cff65a638f17401405821c8a
+size 43329

Lunar-Lander-v1/pytorch_variables.pth

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

Lunar-Lander-v1/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: 1.23.5
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

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: 251.27 +/- 20.01
+      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 0x7c2fd0efd900>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c2fd0efd990>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c2fd0efda20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c2fd0efdab0>", "_build": "<function ActorCriticPolicy._build at 0x7c2fd0efdb40>", "forward": "<function ActorCriticPolicy.forward at 0x7c2fd0efdbd0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c2fd0efdc60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c2fd0efdcf0>", "_predict": "<function ActorCriticPolicy._predict at 0x7c2fd0efdd80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c2fd0efde10>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c2fd0efdea0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c2fd0efdf30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c2fd0ef8fc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1691856922484089188, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAALNML72oFZU/yivMvcc36L7vijy93dfMvAAAAAAAAAAAmntmPe17tT8au10+vXmWvt2Gjz3W0/A9AAAAAAAAAAAzbzg97XkjPmAZhryyZkO+qbzbO84cWb0AAAAAAAAAADMqDz5Eyhw/SrbGvRnaxL4NCQM9xpYJvgAAAAAAAAAA04A1Pm7IjT8PNiQ+9RDrvmJJID7aoe28AAAAAAAAAADNET69DtCaP7Kngr0KjO++P4fvvf6HIDwAAAAAAAAAADOUeb3UAaE92vsePtTQV77wO3o8vC0/OwAAAAAAAAAAM3lwvNttKz/1Kuu9vR+FvntXcb3hckO8AAAAAAAAAADmqyW9eimZPuw8uT1wApy+YOcCPZZ4fz0AAAAAAAAAAEDhFz7kYrg/MBoPP5kGgb5uBOg9tvmePgAAAAAAAAAAZt7UO9IypDwtyGi9/Motvvf8IL1D+o89AAAAAAAAAADmFKs98CKkPuUA3r2/pZy+N8HmvfZ4SToAAAAAAAAAAM1yiTx7apO6Bi8ntnOX8LB2WX46Hr8/NQAAgD8AAIA/5nAkPa67tboGPVM83y6OPJfaNLu+H3c9AACAPwAAgD9SUYi+HoaWP5bHib5tXLK+pAOQvo4d7r0AAAAAAAAAAOYd8j2ayiE/4t5uvX84n77MTSo9wgTyuwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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:": "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

results.json

@@ -0,0 +1 @@
+{"mean_reward": 251.2694779, "std_reward": 20.011707774206712, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-12T17:16:22.887902"}