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: 269.20 +/- 14.89
+      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 0x7b368d1cca60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b368d1ccaf0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b368d1ccb80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b368d1ccc10>", "_build": "<function ActorCriticPolicy._build at 0x7b368d1ccca0>", "forward": "<function ActorCriticPolicy.forward at 0x7b368d1ccd30>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b368d1ccdc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b368d1cce50>", "_predict": "<function ActorCriticPolicy._predict at 0x7b368d1ccee0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b368d1ccf70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b368d1cd000>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b368d1cd090>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b36aaf38d00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1692373940681068453, "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:": "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:b5461d969cd60d581e3cc436a998694e96d8bbe0e088e627b7197c5517bcf8ad
+size 146734

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 0x7b368d1cca60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b368d1ccaf0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b368d1ccb80>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b368d1ccc10>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b368d1ccca0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b368d1ccd30>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b368d1ccdc0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b368d1cce50>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b368d1ccee0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b368d1ccf70>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b368d1cd000>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b368d1cd090>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b36aaf38d00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1692373940681068453,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e5cac63fd51680e63b4a008a47592276e06382aae60b8b9facd8709868ec4e6c
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1071c47d500ceb857080f0a8b5e99d53c0ffb47351f95cd6a411375804743211
+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": 269.2013124, "std_reward": 14.88983067615321, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-08-18T16:22:56.068260"}