package-to-hub the 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: 268.34 +/- 15.17
+      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 0x7fd64b1381f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd64b138280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd64b138310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd64b1383a0>", "_build": "<function ActorCriticPolicy._build at 0x7fd64b138430>", "forward": "<function ActorCriticPolicy.forward at 0x7fd64b1384c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd64b138550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd64b1385e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd64b138670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd64b138700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd64b138790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd64b138820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fd64b2ce800>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1717366924720161467, "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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:": "gAWVIAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHGrCMkyDZmMAWyUTTIBjAF0lEdAlkMTURWcSXV9lChoBkdAcXMHFPznR2gHTRUBaAhHQJZEOJrLyMF1fZQoaAZHQHHey7K7qY9oB01tAWgIR0CWRK4qwyIpdX2UKGgGR0BzDZ7qptJnaAdNNAFoCEdAlkTvkJa7mXV9lChoBkdAb7+EcsDnvGgHTQIBaAhHQJZFDHAAQxx1fZQoaAZHQHErW34Kx9poB0vqaAhHQJZFREofCAN1fZQoaAZHQG6d2ovSMLpoB00RAWgIR0CWRUryUcGUdX2UKGgGR0BwO4S26TW5aAdNCwFoCEdAlkWnsolUqHV9lChoBkdAMiuws5GSZGgHS8BoCEdAlkXHkYGdJHV9lChoBkdAcA0hky1uzmgHTTYCaAhHQJZJCRvFWGR1fZQoaAZHQHJpVoL5RCRoB0v8aAhHQJZJ7FzdUKl1fZQoaAZHQG9/OUUwi7loB006AWgIR0CWSgdmQKa5dX2UKGgGR0BwuiMtK7I1aAdNCgFoCEdAlkqDst03fnV9lChoBkdAbn25Jbt7bGgHTRwBaAhHQJZKvDMvAXV1fZQoaAZHQHMLZJoTPB1oB00QAWgIR0CWS/67/XGwdX2UKGgGR0BvcfWBjFyaaAdL+2gIR0CWTI/Aj6eodX2UKGgGR0BweAOhCdBjaAdNFAFoCEdAlk0UwFkhBHV9lChoBkdAcZvVLBbfQGgHTSUBaAhHQJZNN1eSjg11fZQoaAZHQHJ0bAxi5NJoB00CAWgIR0CWTU8c+7lJdX2UKGgGR0Bxhz+bVjI8aAdL/2gIR0CWTWCemNzbdX2UKGgGR0By+RBSk0rLaAdNIQFoCEdAlk2158jRlnV9lChoBkdAbvnnB+F10WgHTTwBaAhHQJZOFQvYe1d1fZQoaAZHQG/PZNGmUGFoB02JAWgIR0CWTmeIEbHZdX2UKGgGR0Bw6tH3Dej3aAdL7WgIR0CWUP5dnkDIdX2UKGgGR0BShDx5LRKIaAdLnmgIR0CWUP3Sro4ddX2UKGgGR0ByeRoi9qUNaAdNDQFoCEdAllEUFOfukXV9lChoBkdAcENWZ7Xxv2gHTYMCaAhHQJZR9T72tdR1fZQoaAZHQHA5SKBNEgJoB00RAWgIR0CWUgSYw7DEdX2UKGgGR0BwFyBun/DMaAdNAQFoCEdAllJVgH/tIHV9lChoBkdAcDk7D2rXDmgHTQEBaAhHQJZTcjbBXS11fZQoaAZHQHCtaQNkOI9oB01IAWgIR0CWVDPJaJQ+dX2UKGgGR0BydbB1s+FDaAdNAgFoCEdAllSpRKpT/HV9lChoBkdAcVAbiIcin2gHTRsBaAhHQJZVoTVUdaN1fZQoaAZHQHCNJd4Vym1oB00CAWgIR0CWVbd5Y5ktdX2UKGgGR0BVBdAX2ugZaAdN6ANoCEdAllXOKoAGS3V9lChoBkdAcTgovi97GGgHTSwBaAhHQJZWFdmg8KZ1fZQoaAZHQHHBvUvwmVtoB000AWgIR0CWVjkgOjIrdX2UKGgGR0Bw2Din5zo2aAdNIQFoCEdAllZMFQl8gXV9lChoBkdAcrvObAk9lmgHS9xoCEdAllf3gUDdQHV9lChoBkdAcZN+mFaje2gHS9RoCEdAlljchTwUg3V9lChoBkdAcTYfnwG4Z2gHS/1oCEdAllkjF2mpEXV9lChoBkdAcgWzOoo/imgHS/9oCEdAlllQVGkN4XV9lChoBkdAbzWgq3EycmgHS/VoCEdAllnrQC0WuXV9lChoBkdAbzzjWCmMwWgHTXkBaAhHQJZaKgi/wiJ1fZQoaAZHQG9MJtix3V1oB0v2aAhHQJZaTWkJrtV1fZQoaAZHQHLjLIcR15loB00BAWgIR0CWbiT3IuGsdX2UKGgGR0BEPgq/dqL1aAdLuGgIR0CWbmZAIIGAdX2UKGgGR0Bxvz8rI5o5aAdNGAFoCEdAlm9TPGACn3V9lChoBkdAcTwemelKsmgHS/NoCEdAlm/Q2hqTKXV9lChoBkdAb9x1V5rxiGgHS/5oCEdAlm/ju0CzTnV9lChoBkdAcnQA8B+4LGgHS/JoCEdAlm/zZDiOvXV9lChoBkdALaI+fRNRFmgHS5loCEdAlm/13+uNgnV9lChoBkdAciBPEsJ6Y2gHTRkBaAhHQJZwcT9KmKt1fZQoaAZHQHAxx9PUKAtoB0v5aAhHQJZxdh3JPqN1fZQoaAZHQHK1yQcPvrpoB0vwaAhHQJZyG8TSLIh1fZQoaAZHQHGDZPqLS/loB0vfaAhHQJZyRV3ljmV1fZQoaAZHQG+xywGGEf1oB0vkaAhHQJZym6UaAFx1fZQoaAZHQHB8pflZHNJoB00TAWgIR0CWcs9LYf4idX2UKGgGR0BxHx2GIsRQaAdL5GgIR0CWdGUxEfDDdX2UKGgGR0BufBFocrAhaAdNZAFoCEdAlnaWr0aqCHV9lChoBkdAcdDBz3h4uGgHTQIBaAhHQJZ2lwLmZE51fZQoaAZHQG5mD5TIeYFoB001AWgIR0CWdrJUYKpldX2UKGgGR0Bw3q6BiCrcaAdNAAFoCEdAlnc8fFJg9nV9lChoBkdAcWGPWhAWzmgHTQUBaAhHQJZ3XmHP/rB1fZQoaAZHQG+5vXTVlPJoB00MAWgIR0CWd23lCCz1dX2UKGgGR0BvflL39JjEaAdNCwFoCEdAlngdBBzFM3V9lChoBkdAcdbvysjmjmgHTSkBaAhHQJZ4S6J66at1fZQoaAZHQHIJwAhje9BoB00GAWgIR0CWeQlzEJjUdX2UKGgGR0BwiIGZ/kNnaAdNBAFoCEdAlnnXRLK3eHV9lChoBkdAb+EoxYaHbmgHS/doCEdAlnoPdRBNVXV9lChoBkdAcisikfs/p2gHTQgBaAhHQJZ6ULNOdoZ1fZQoaAZHQHCDoDgZTAFoB01eAWgIR0CWfE1SflIVdX2UKGgGR0Bw51diUgSwaAdNDAFoCEdAlnxk/SpiqnV9lChoBkdAb1scslLOA2gHS/1oCEdAln4lMh5gPXV9lChoBkdAcKC0CRwIdGgHS+1oCEdAln6Bmf5DZ3V9lChoBkdAcconKnvUjWgHS/toCEdAln8SO7xusXV9lChoBkdAb+I7yQPqcGgHTQUBaAhHQJZ/NW912aF1fZQoaAZHQHD9KraM72doB00fAWgIR0CWf1e2uxKQdX2UKGgGR0BunH/rB0p3aAdNAwFoCEdAloBjkhib2HV9lChoBkdAcXhI1tO2zGgHTU8BaAhHQJaBBuGbkOt1fZQoaAZHQHFAlz+3pfRoB00NAWgIR0CWgaGyX2M9dX2UKGgGR0Bu7my/sVtXaAdL9mgIR0CWgcuWa+ewdX2UKGgGR0BxHpfjS5RTaAdNBwFoCEdAloLfnKW9lHV9lChoBkdAS+8AvL5h0GgHS8loCEdAloMe6mO2iXV9lChoBkdAcmxUkfLcK2gHTSYBaAhHQJaDkOc2BJ91fZQoaAZHQHIGA8SwnploB0vbaAhHQJaDm43FUAF1fZQoaAZHQFEt1EVnEl5oB03oA2gIR0CWhZVrAP/adX2UKGgGR0BT1/VurIYFaAdN6ANoCEdAloaBMvh60XV9lChoBkdAccqA2ycCo2gHTQQBaAhHQJaG+n0kGA11fZQoaAZHQHI3FEiMYMxoB0voaAhHQJaH7VbzK9x1fZQoaAZHQHFxrThHbypoB00fAWgIR0CWiJxN7BwddX2UKGgGR0BxuymygPEsaAdNJAFoCEdAlokCyUs4DXV9lChoBkdAcCiAP/aQFWgHTSIBaAhHQJaJHZbpu/F1fZQoaAZHQHMDiliz9jxoB0v7aAhHQJaJn2AXl8x1fZQoaAZHQHB1y7kGRmtoB0vWaAhHQJaKaAqd6LR1fZQoaAZHQG3Dv9UCJXRoB0vsaAhHQJaLn+S8rZt1fZQoaAZHQHBGD2i+L3toB00ZAWgIR0CWi8HARChOdX2UKGgGR0BwVQUqQRwqaAdNJQFoCEdAlowIJAt4A3V9lChoBkdAccL71qWTo2gHS/xoCEdAloy/W+XZ5HV9lChoBkdAT0IGhVU+92gHS7NoCEdAlo2Chi9ZinVlLg=="}, "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:": "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-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:a4674deafc90dc1eabf1128ea5ead62849e52f5a732c08313344cfb1f7495e28
+size 148040

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 0x7fd64b1381f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd64b138280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd64b138310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd64b1383a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fd64b138430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fd64b1384c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd64b138550>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd64b1385e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fd64b138670>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd64b138700>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd64b138790>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd64b138820>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fd64b2ce800>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1717366924720161467,
+    "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:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAEAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////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:": "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:": "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:f1e622e7c56aaa802689495b7efd8bfae7b36601ce2bf3209acc37e198d1756f
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70f54b80a853c7bb81a79fe5184e0f237d499667de5215632cf2f1334e1000eb
+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": 268.3423987, "std_reward": 15.171304152116369, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-06-02T23:14:51.681150"}