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: 260.75 +/- 21.57
+      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 0x7d8c9a520af0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d8c9a520b80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d8c9a520c10>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d8c9a520ca0>", "_build": "<function ActorCriticPolicy._build at 0x7d8c9a520d30>", "forward": "<function ActorCriticPolicy.forward at 0x7d8c9a520dc0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d8c9a520e50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d8c9a520ee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7d8c9a520f70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d8c9a521000>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d8c9a521090>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d8c9a521120>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d8ca3bc1ec0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1694041054556005800, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAABqDQ73O1pY96DXNvW1KcL7cnUi9kWufvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_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": 3908, "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": 1, "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-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:d9d7fc5b55763c1f0e7acec3682b4e680e90d608bb9dd2f435c8e7a399f90eab
+size 146092

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 0x7d8c9a520af0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d8c9a520b80>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d8c9a520c10>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d8c9a520ca0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7d8c9a520d30>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7d8c9a520dc0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d8c9a520e50>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d8c9a520ee0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7d8c9a520f70>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d8c9a521000>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d8c9a521090>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d8c9a521120>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7d8ca3bc1ec0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1694041054556005800,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAABqDQ73O1pY96DXNvW1KcL7cnUi9kWufvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+    },
+    "_last_episode_starts": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.00044800000000000395,
+    "_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": 3908,
+    "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": 1,
+    "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:": "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-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3476722bb68c2220ff13f02a83d1d34a5c5ef028d5e8c27260d0aa9c72150db1
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1b4d9408556a15a0b185e44183e6ff3ee79e75a27bfd9b60b40ca15e75d2d58d
+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": 260.7463277710425, "std_reward": 21.567572195959844, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-09-06T23:39:09.583879"}