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: 269.62 +/- 88.16
+      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 0x7977d3385120>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7977d33851b0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7977d3385240>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7977d33852d0>", "_build": "<function ActorCriticPolicy._build at 0x7977d3385360>", "forward": "<function ActorCriticPolicy.forward at 0x7977d33853f0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7977d3385480>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7977d3385510>", "_predict": "<function ActorCriticPolicy._predict at 0x7977d33855a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7977d3385630>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7977d33856c0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7977d3385750>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7977d4201800>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1694380972392891232, "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": 817, "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"}}

ppo-lunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:44cedb8f30f02badbcd71fb3ff7a80532d55ba5271a8ae896b15435ec82997ef
+size 146630

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 0x7977d3385120>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7977d33851b0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7977d3385240>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7977d33852d0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7977d3385360>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7977d33853f0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7977d3385480>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7977d3385510>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7977d33855a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7977d3385630>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7977d33856c0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7977d3385750>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7977d4201800>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1694380972392891232,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAJpxa7zXo2K7lxIhPK+dnjyXRZg8Bn2HvQAAgD8AAIA/AOShOxjQjT0uqu29Gq/Kvvu7Tr5c6bs8AAAAAAAAAAAzcz+6XJs3uu5tSLO6gy+wuEtHuxq/yzMAAIA/AACAPwDzwjyo5DQ/mJ8FvttA5b4ZK4E9RTfrvQAAAAAAAAAAMxuIuyODJz36LYu9V2G1vs0lAr6tBbE7AAAAAAAAAADNhNK8/QwLPmCBBb7NwuW+F3pcvl1LwbsAAAAAAAAAADOq5rwKwlk/6ypYvViuAL/CXus8Rer5PAAAAAAAAAAAAAYLPGJMjD9xoQI95MT9vpHG8z0y4bW7AAAAAAAAAACNBdE9+siXP9rfHj6QPu6+rLKbPqs4tz0AAAAAAAAAAABN2jxzdLU/nooXPhSxK75bM3s9GTQ8PgAAAAAAAAAAMxLUvR7H+D1vAYa9ED2/vtiOH74m41U6AAAAAAAAAACzRjQ9+LYDP75d/r2D9u++CWsTvZZ7l70AAAAAAAAAAM1GHLz2NFs9Eu1DPVpsob6Dci69fVmsvAAAAAAAAAAAzayoO88kLbx76Mo7IoOFPEGejj3tc129AACAPwAAgD9mSOa89hJIvBrRLT19lwC9hRKQPaouAj4AAIA/AACAP+Y6Wj1cS366AR2BtZB1b7IgpCa6rjbTNAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": 817,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "gAWVcAIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoB4wCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoECiWCAAAAAAAAAABAQEBAQEBAZRoFEsIhZRoGHSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBAoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaApLCIWUaBh0lFKUjARoaWdolGgQKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgKSwiFlGgYdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:de31eaa3159710fd9d06e43f3c9805ec129a77d2935cb5ba994fd64b1a9d4ca6
+size 87929

ppo-lunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:303e686813b38213e4db73c1bb580aed869a7d7b86f2a947bfe2be5afa504a84
+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-5.15.109+-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": 269.6214989559774, "std_reward": 88.1584194204096, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-10T22:37:01.294680"}