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: 276.62 +/- 19.85
+      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 0x7b07cdc1c3a0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b07cdc1c430>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b07cdc1c4c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b07cdc1c550>", "_build": "<function ActorCriticPolicy._build at 0x7b07cdc1c5e0>", "forward": "<function ActorCriticPolicy.forward at 0x7b07cdc1c670>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b07cdc1c700>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b07cdc1c790>", "_predict": "<function ActorCriticPolicy._predict at 0x7b07cdc1c820>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b07cdc1c8b0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b07cdc1c940>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b07cdc1c9d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7b07cdbc1f40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717693303830504594, "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": 496, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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": 8, "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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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "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:f9c570c302f9f31962d5ea683ecfc23f7d3130d787f998c1082d983a63000adc
+size 148004

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 0x7b07cdc1c3a0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7b07cdc1c430>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7b07cdc1c4c0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7b07cdc1c550>",
+        "_build": "<function ActorCriticPolicy._build at 0x7b07cdc1c5e0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7b07cdc1c670>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7b07cdc1c700>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7b07cdc1c790>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7b07cdc1c820>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7b07cdc1c8b0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7b07cdc1c940>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7b07cdc1c9d0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7b07cdbc1f40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1717693303830504594,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAHNvsL05OwA+WbeLPgGma74nyaI9lqegPAAAAAAAAAAAZi29vFzffz02/rI+675FvkBYYj42izG9AAAAAAAAAAAzHUQ9M7AyP2Bshb1lc82+AguSPGfqh70AAAAAAAAAAKZcqD1e5BU/aRA0OwIdwb5RqSw9iYw7vQAAAAAAAAAAGoemvd5L/z0OJuE9VyBnvktrJDy6LBY9AAAAAAAAAAD6zje+xy48P/7yir2whgm/Q1Fpvop1ET0AAAAAAAAAAACqRz5eHqE/RhPEPpr9Cr9vWLQ+VYvfPQAAAAAAAAAAZpqaPlLTij+yq0A+TJ4Gv8Weyz59vWi9AAAAAAAAAABmKjw+1MP8PqVHYr52ZbO+qMx7PXdsiL0AAAAAAAAAABqPVL0sFoY8JtG5PQLML77xP787SL7WvAAAAAAAAAAATQ0DvWw+uj9qZCO/EWCtPvBp2zwm+p89AAAAAAAAAACT4GQ+hFwCPziLt77hvqu+2aqnu1Vd570AAAAAAAAAABpjtL1xG3U8uPNJPk+mR77LN8M95njLvAAAAAAAAAAAAICSvck7zz64LeE9OgO1vnlgfLyoJkw9AAAAAAAAAACN86890v1SPpZOSr6xX3y+hVeqvSSxkLsAAAAAAAAAALZPXb4tr8E+9pYxPm7jjL4mut69nIg7PgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": 496,
+    "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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": 8,
+    "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:b9f691d24fbfec946efa778af7274a94222313a0e0492c0d16995b472df10dd4
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1062d2c069e742a315d7a6dc5c9f8dc650182b4544eab79ed3afe77427701c89
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Apr 28 14:29:16 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.3.0+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 276.6207391, "std_reward": 19.849490172361673, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-06T17:28:38.527198"}