inittial PPO

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: 268.56 +/- 61.35
+      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 0x78a29d774ee0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78a29d774f70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78a29d775000>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78a29d775090>", "_build": "<function ActorCriticPolicy._build at 0x78a29d775120>", "forward": "<function ActorCriticPolicy.forward at 0x78a29d7751b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x78a29d775240>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78a29d7752d0>", "_predict": "<function ActorCriticPolicy._predict at 0x78a29d775360>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78a29d7753f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78a29d775480>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x78a29d775510>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x78a29e22e300>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1724018081631932558, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAABAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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:": "gAWV4QsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHDAWVu76HmMAWyUS9mMAXSUR0CkSrYL1EmZdX2UKGgGR0Bx4ywfQrtmaAdLymgIR0CkS2PBrN4adX2UKGgGR0BvXq8an753aAdLy2gIR0CkS3mG21D0dX2UKGgGR0BxQTkfcN6PaAdL2mgIR0CkS5vyCnP3dX2UKGgGR0By7golUp/gaAdL5GgIR0CkS7JiI+GHdX2UKGgGR0Byz1agVXV9aAdL52gIR0CkS8OUdJardX2UKGgGR0BxaRtcfNiZaAdLw2gIR0CkS8iOearndX2UKGgGR0Bw7lE3Kji5aAdL3mgIR0CkS/GC7K7qdX2UKGgGR0Bxc0t6HCXQaAdLuWgIR0CkTEazE74jdX2UKGgGR0BxA/XK8tf5aAdLwmgIR0CkTFXF98Z2dX2UKGgGR0Bvrw1FYuCgaAdLz2gIR0CkTHufNA1OdX2UKGgGR0BwzsT/Q0GeaAdL3WgIR0CkTIk4m1IAdX2UKGgGR0BwD+yon8baaAdLxWgIR0CkTM2P1ct5dX2UKGgGR0By08Y51eSkaAdLxmgIR0CkTNBA4XGfdX2UKGgGR0BzqEJ5VwPzaAdL32gIR0CkTUb04BFNdX2UKGgGR0B0Sz3dsSCfaAdL2GgIR0CkTY/Wcz68dX2UKGgGR0ByAfWH1vl2aAdLzGgIR0CkTh6O5rgwdX2UKGgGR0BwOjn7pFCtaAdLwWgIR0CkTlwyqMm4dX2UKGgGR0Bw9czTF2mpaAdL2mgIR0CkTmlSCOFQdX2UKGgGR0BxsY1VHWjHaAdLvWgIR0CkTnuUt7KJdX2UKGgGR0BGkramGdqdaAdLo2gIR0CkTnuUD+zddX2UKGgGR0BwMKpeeFtbaAdL52gIR0CkTt7Ou7pWdX2UKGgGR0BybETrVvuPaAdL9mgIR0CkTu14gRsedX2UKGgGR0BzEr60pmVaaAdL/WgIR0CkTxnBLwnZdX2UKGgGR0Bwmvra/RE4aAdL1mgIR0CkTztVR1oydX2UKGgGR0BwChYSxqwhaAdL12gIR0CkWGMOoYNzdX2UKGgGR0BzEKd1+y7gaAdL2WgIR0CkWHclHBk7dX2UKGgGR0ByB4FW4mTlaAdLz2gIR0CkWKMz2vjfdX2UKGgGR0Bwi+w+t8u0aAdL0mgIR0CkWK3tBv74dX2UKGgGR0BxH1B0IToMaAdLyGgIR0CkWQ9ECvHMdX2UKGgGR0Bn89eMQ2/BaAdN6ANoCEdApFkV6cAimnV9lChoBkdAcLsiQDFId2gHS8loCEdApFlRzYEns3V9lChoBkdAbffEGZ/kNmgHS8poCEdApFnWAuqWC3V9lChoBkdAbwMrcTJyQ2gHS8poCEdApFoGthd+onV9lChoBkdAbo0J53Tuv2gHS9VoCEdApFpBT4tYjnV9lChoBkdAcCp+9Jz1b2gHS8NoCEdApFphn3+MqHV9lChoBkdAcSciKiwjdGgHS+FoCEdApFpnh0hePnV9lChoBkdAceuPU8V58mgHS+poCEdApFpyxzJZGXV9lChoBkdAcTmpu/Dcd2gHS89oCEdApFqQxcmjTXV9lChoBkdAc1nJ+DvmYGgHS99oCEdApFrlhsqJ/HV9lChoBkdAch3f6oESumgHS9doCEdApFrqfe1rqXV9lChoBkdAcTpL/0dzXGgHS81oCEdApFryWmgrY3V9lChoBkdAcrU+4smOVGgHS7toCEdApFr8Ht4RmXV9lChoBkdAcZZInjQzDWgHS91oCEdApFsuCyyD7XV9lChoBkdAbvwZWJaaC2gHS9RoCEdApFs4o/iYLXV9lChoBkdAcJ79ZzPrwGgHS9VoCEdApFuMSmIj4nV9lChoBkdAcgSx7iQ1aWgHS9VoCEdApFuR9NN8E3V9lChoBkdAc7WvMKTjemgHS9toCEdApFveM4tHx3V9lChoBkdAcf6dcB2fTWgHS9FoCEdApFwyoddVvXV9lChoBkdAcTEpOvdM02gHS89oCEdApFxhwCKaX3V9lChoBkdAcoJNtqHoHWgHS8hoCEdApFypYA80UHV9lChoBkdAcUJlZ5iVjmgHS+VoCEdApFzker+5v3V9lChoBkdAcz/WIoE0SGgHS8ZoCEdApF05SiudPXV9lChoBkdAcX8pFTefqWgHS8hoCEdApF1P6fra/XV9lChoBkdActXRg7YChmgHS/poCEdApF1W2b5M13V9lChoBkdAcPoHTI/7i2gHS+9oCEdApF1ifOD8L3V9lChoBkdAcgDfCyhSL2gHS/loCEdApF1hBX0Xg3V9lChoBkdAceP51eSjg2gHS8NoCEdApF2D3ueBhHV9lChoBkdAcm1H80k4WGgHS+ZoCEdApF2fPcBU73V9lChoBkdAcm82h7E5yWgHS+loCEdApF25VQyhz3V9lChoBkdAcKMVbiZOSGgHS9poCEdApF3PV7Qb/HV9lChoBkdAcm8NKRMewWgHS8BoCEdApF3eyHEdenV9lChoBkdAcYGa99MK1GgHS9ZoCEdApF4UELYwqXV9lChoBkdAdAZ1+y7f52gHS9VoCEdApF5k87p3YHV9lChoBkdAcVgBNEgGKWgHS9doCEdApF6+JDVpbnV9lChoBkdAb9yews5GSmgHS8loCEdApF8EvduYQnV9lChoBkdAc1d704BFNWgHS/1oCEdApF9gxUNrkHV9lChoBkdASWaNS619fGgHS5toCEdApF9msJY1YXV9lChoBkdAcub1V5rxiGgHS9toCEdApF93YvnKXHV9lChoBkdAcwUiO/+Kj2gHS8xoCEdApF+Uq+ajOHV9lChoBkdAby3YLb5/LGgHS8ZoCEdApF+UtNBWxXV9lChoBkdAdBm2VVxS52gHS8JoCEdApF+Zof0VanV9lChoBkdAcAYjMmnfmGgHS8ZoCEdApF+hq46OpHV9lChoBkdAcaHoP07KaGgHS8BoCEdApF/ms3hn8XV9lChoBkdAcgigOBlMAWgHS91oCEdApGACYqoZRHV9lChoBkdAcWpA7gbZOGgHS+xoCEdApGACQvHtGHV9lChoBkdAbryl+mWMTGgHS8hoCEdApGASd1+y7nV9lChoBkdAcCMzv7WNFWgHS8FoCEdApGBByKekHnV9lChoBkdAceQAFPi1iWgHS+9oCEdApGCI40dilXV9lChoBkdAbcE3z+WGAWgHS+BoCEdApGDnqFAVwnV9lChoBkdAcfbrkbPyCmgHS85oCEdApGEKsOoYN3V9lChoBkdAcaaWTot+TmgHS8ZoCEdApGE520Re1XV9lChoBkdAb6DJOFg2ImgHS75oCEdApGF8PDpC8nV9lChoBkdAcP0Awwj+rGgHS9loCEdApGHXg1m8NHV9lChoBkdAcsV+hoM8YGgHS+FoCEdApGInJV81GnV9lChoBkdAb+lNyHVPN2gHS+toCEdApGInaL4ve3V9lChoBkdAcNAxDst03mgHS+RoCEdApGIxrWRRuXV9lChoBkdAcYpxyn1nNGgHS+RoCEdApGI3EfkmyHV9lChoBkdAcpQqRU3n6mgHS+loCEdApGJO/Ho5gnV9lChoBkdAbkrw2l2vCGgHS8hoCEdApGJWZNO/L3V9lChoBkdAcpmjbBXS0GgHS9NoCEdApGJZgmZ3LXV9lChoBkdAcwzIE8q4IGgHS85oCEdApGJmJk5IYnV9lChoBkdAcTnB3A2ycGgHS91oCEdApGKc52hZhnV9lChoBkdAcHd9P1tfomgHS8xoCEdApGKiP6sQunV9lChoBkdAcc67YkE9uGgHS9RoCEdApGL+4PPLPnV9lChoBkdAcTJ0Cih37mgHS7RoCEdApGMD5AQg93V9lChoBkdAcuibBGhEjWgHS9RoCEdApGOEgjhUBHV9lChoBkdAcUq+Q2dd3WgHS+ZoCEdApGPqYXwb2nV9lChoBkdAcsVwxFiKBWgHS8VoCEdApGQhv1lGw3V9lChoBkdAcseBEKE39GgHS/NoCEdApGRZGBnSOXVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 744, "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": 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-5.15.154+-x86_64-with-glibc2.31 # 1 SMP Thu Jun 27 20:43:36 UTC 2024", "Python": "3.10.13", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.2", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.0.0", "Gymnasium": "0.28.1", "OpenAI Gym": "0.26.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5d3168e2fa4afc790a9a040191a862c7b0955df5ce8a6b7bed73b5625918dd72
+size 147864

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 0x78a29d774ee0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x78a29d774f70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x78a29d775000>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x78a29d775090>",
+        "_build": "<function ActorCriticPolicy._build at 0x78a29d775120>",
+        "forward": "<function ActorCriticPolicy.forward at 0x78a29d7751b0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x78a29d775240>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x78a29d7752d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x78a29d775360>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x78a29d7753f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x78a29d775480>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x78a29d775510>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x78a29e22e300>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1724018081631932558,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAABAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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": 744,
+    "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": 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:ac38e567b9ad10a71a9cfb961b9ffd03925f7831e332b57d5bde1dedf53a2700
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0eb905684ca831918234d387ce81399382de07a10adacada88291225aca20e8f
+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-5.15.154+-x86_64-with-glibc2.31 # 1 SMP Thu Jun 27 20:43:36 UTC 2024
+- Python: 3.10.13
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.1.2
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.0.0
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.26.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 268.56191739999997, "std_reward": 61.34875281940061, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-08-18T22:33:46.416012"}