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: 257.56 +/- 19.86
+      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 0x7f234f6b4670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f234f6b4700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f234f6b4790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f234f6b4820>", "_build": "<function ActorCriticPolicy._build at 0x7f234f6b48b0>", "forward": "<function ActorCriticPolicy.forward at 0x7f234f6b4940>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f234f6b49d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f234f6b4a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f234f6b4af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f234f6b4b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f234f6b4c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f234f6b4ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f234f855880>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717155434737969872, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAK2eA76S60o+SjsePnKZOr6YfrA8SguLPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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-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 sha256:36b4d919c0dbd566df2ee4e42869398f2524af81838bc8ea07a57414fb8dbaae
+size 147410

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 0x7f234f6b4670>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f234f6b4700>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f234f6b4790>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f234f6b4820>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f234f6b48b0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f234f6b4940>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f234f6b49d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f234f6b4a60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f234f6b4af0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f234f6b4b80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f234f6b4c10>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f234f6b4ca0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f234f855880>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1000448,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1717155434737969872,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAK2eA76S60o+SjsePnKZOr6YfrA8SguLPQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bc4926bc06b945addea67a98c282af805a7d6b6bfcc1cfbf184721e8dcfaf1fe
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a954ab8e5c3afe9f57cc59e5cd709e3d16b3e2d895581b00e6eb153e5433c27
+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": 257.5630642074275, "std_reward": 19.862482823380976, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-05-31T12:10:33.194241"}