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: 239.74 +/- 25.36
+      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 0x7df26ba38430>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7df26ba384c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7df26ba38550>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7df26ba385e0>", "_build": "<function ActorCriticPolicy._build at 0x7df26ba38670>", "forward": "<function ActorCriticPolicy.forward at 0x7df26ba38700>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7df26ba38790>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7df26ba38820>", "_predict": "<function ActorCriticPolicy._predict at 0x7df26ba388b0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7df26ba38940>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7df26ba389d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7df26ba38a60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7df26ba581c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1696764607526596535, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 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 sha256:112b52888eb9ae749f54482ec5e64c52d66707a533e732ac3f25295ed06391fe
+size 146755

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 0x7df26ba38430>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7df26ba384c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7df26ba38550>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7df26ba385e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7df26ba38670>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7df26ba38700>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7df26ba38790>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7df26ba38820>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7df26ba388b0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7df26ba38940>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7df26ba389d0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7df26ba38a60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7df26ba581c0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1696764607526596535,
+    "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:": "gAWVQwwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQGad8z67/XKMAWyUTegDjAF0lEdAqrO84zabnXV9lChoBkdAZNq+/QBxP2gHTegDaAhHQKq1i6BiCrd1fZQoaAZHQGXS2V3Ux21oB03oA2gIR0Cqu0WYnfEXdX2UKGgGR0BiETt9hJAdaAdN6ANoCEdAqr++m+Cbt3V9lChoBkdAZEHkQPI4l2gHTegDaAhHQKrCVmcOLBN1fZQoaAZHQGR0AX/HYHxoB03oA2gIR0Cqx86Q3gk1dX2UKGgGR0BiKWTRplBhaAdN6ANoCEdAqsgpDVpblnV9lChoBkdAXxutaIN3GGgHTegDaAhHQKrKr+CsfaJ1fZQoaAZHQGIJ2X9itq5oB03oA2gIR0CqzQMIu5BkdX2UKGgGR0Bji8deY2KmaAdN6ANoCEdAqs0ZFG5MDnV9lChoBkdAYH/4MWoFV2gHTegDaAhHQKrN0pcX3xp1fZQoaAZHQGXkP9tMwlBoB03oA2gIR0CqzfE4m1IAdX2UKGgGR0BlWwZwXIluaAdN6ANoCEdAqs4D/lyR0XV9lChoBkdAX1/6+FlCkWgHTegDaAhHQKrOFc6eXiR1fZQoaAZHQGZej3/Pw/hoB03oA2gIR0Cq3KgeA/cGdX2UKGgGR0Bk9F2JSBK+aAdN6ANoCEdAqt7D8P4EfXV9lChoBkdAYuZ37k4m1WgHTegDaAhHQKre0vOhTOx1fZQoaAZHQGCS9JjDsMRoB03oA2gIR0Cq4CoRAbADdX2UKGgGR0Bj+xP69CeFaAdN6ANoCEdAquQN6ol2NnV9lChoBkdAZdVocrAgxWgHTegDaAhHQKrnUMQ2/BZ1fZQoaAZHQGBTkdeY2KloB03oA2gIR0Cq6cEvboKVdX2UKGgGR0BhLFbor4FiaAdN6ANoCEdAqu7v7BO58XV9lChoBkdAXyHV7Qb++GgHTegDaAhHQKrvTtu1ndx1fZQoaAZHQGGGnhKlHjJoB03oA2gIR0Cq8lS9VWCFdX2UKGgGR0Bhbi/CZWq+aAdN6ANoCEdAqvWNM9KVZHV9lChoBkdAYBmbp/wy7GgHTegDaAhHQKr1rECvHLl1fZQoaAZHQGeuP+GXXy1oB03oA2gIR0Cq9rhr30wrdX2UKGgGR0BjeMxM36yjaAdN6ANoCEdAqvbg/C66KHV9lChoBkdAZpTUUfxMFmgHTegDaAhHQKr2/M6ij+J1fZQoaAZHQGHwmelKsdVoB03oA2gIR0Cq9xbBoEjgdX2UKGgGR0BnCSInBtUGaAdN6ANoCEdAqvwqFTNt7HV9lChoBkdAWsIRkEs8PmgHTegDaAhHQKsGYjmjj711fZQoaAZHQGGaSCFsYVJoB03oA2gIR0CrBnJS75EddX2UKGgGR0Bm5sD+zdDZaAdN6ANoCEdAqwf0B0ZFX3V9lChoBkdAZSuBjFyaNWgHTegDaAhHQKsL82kSElF1fZQoaAZHQGc+c+A3DN1oB03oA2gIR0CrDyuPeYUndX2UKGgGR0BiAr0Dlo12aAdN6ANoCEdAqxI6R6nivXV9lChoBkdAbrvx82JizGgHTeACaAhHQKsXF5CWu5l1fZQoaAZHQFy/5nlGPPtoB03oA2gIR0CrGOKOT7l8dX2UKGgGR0BdoVCgK4QSaAdN6ANoCEdAqxlb5TIeYHV9lChoBkdARAqbKA8SwmgHTR8BaAhHQKsaRLZBcA11fZQoaAZHQGMITsQd0aJoB03oA2gIR0CrG9RXfZVXdX2UKGgGR0Bk0EaZQYUGaAdN6ANoCEdAqx3mWUr08XV9lChoBkdAYrsaRZEDyWgHTegDaAhHQKsd+f4AS391fZQoaAZHQGQdK28Zk09oB03oA2gIR0CrHp+xOclPdX2UKGgGR0Bgncy57PY4aAdN6ANoCEdAqx7MMd92HXV9lChoBkdAXB7H0btJF2gHTegDaAhHQKse3WLgn+h1fZQoaAZHQGTWbxEv0yxoB03oA2gIR0CrImIxYaHcdX2UKGgGR0BnXa5VfeDWaAdN6ANoCEdAqyyX9WIXTHV9lChoBkdAZeQCHymQ82gHTegDaAhHQKssp1dPci51fZQoaAZHQGRc4f4h2W9oB03oA2gIR0CrLgdUsFt9dX2UKGgGR0Bj8kyzollcaAdN6ANoCEdAqzNodp7CznV9lChoBkdAYHdAi3XqaGgHTegDaAhHQKs60g4ffXR1fZQoaAZHQGYlN96Tnq5oB03oA2gIR0CrPuyJCSiedX2UKGgGR0BwK664Ds+naAdNBgNoCEdAq0AGfbsWwnV9lChoBkdAYJRD1GsmwGgHTegDaAhHQKtAWU9pyp91fZQoaAZHQGMz+kYXO4ZoB03oA2gIR0CrQK5KODJ2dX2UKGgGR0BjwQDV6NVBaAdN6ANoCEdAq0FllZowmHV9lChoBkdAZEHcnE2pAGgHTegDaAhHQKtC/DkU9IR1fZQoaAZHQGCbZmI0qH5oB03oA2gIR0CrRSMw1zhhdX2UKGgGR0Bk9g0waisXaAdN6ANoCEdAq0U4etCAtnV9lChoBkdAYW7863iJf2gHTegDaAhHQKtGDGPPszF1fZQoaAZHQGR73YL9deJoB03oA2gIR0CrRh+irT6SdX2UKGgGR0BivadnTRYzaAdN6ANoCEdAq0oCTW5H3HV9lChoBkdAY3eAkLQXymgHTegDaAhHQKtXI8jAzpJ1fZQoaAZHQGOm5+pfhMtoB03oA2gIR0CrVzO2RaHLdX2UKGgGR0BkljwDvE0jaAdN6ANoCEdAq1ibYbsF+3V9lChoBkdAcCGgNPP9k2gHTeMBaAhHQKtZD3A2ycF1fZQoaAZHQGdJeTV2A5JoB03oA2gIR0CrW/D9fkWAdX2UKGgGR0Blocm0E5hjaAdN6ANoCEdAq2DA3zcynHV9lChoBkdAZkOoGY8dP2gHTegDaAhHQKtkIaWom5V1fZQoaAZHQGKucGTs6aNoB03oA2gIR0CrZQQdKdxydX2UKGgGR0Bhsl34bjtHaAdN6ANoCEdAq2VOyiVSoHV9lChoBkdAXXireZXuE2gHTegDaAhHQKtlmTakAPx1fZQoaAZHQGQxOYQarFRoB03oA2gIR0CrZ8YuscQzdX2UKGgGR0BiXJbD/EOzaAdN6ANoCEdAq2nqNOuaF3V9lChoBkdAZwy274BV/GgHTegDaAhHQKtp/vAGjbl1fZQoaAZHQGN2IIWxhUloB03oA2gIR0CravMFUyYYdX2UKGgGR0BdqcTJyQxOaAdN6ANoCEdAq2sPjU/fO3V9lChoBkdAZlwon8baRWgHTegDaAhHQKtwo4XGff51fZQoaAZHQGPPZoPCl8BoB03oA2gIR0CrfGwA2hqTdX2UKGgGR0BlfffZVXFMaAdN6ANoCEdAq3x9THbRGHV9lChoBkdAYKQAQxveg2gHTegDaAhHQKt+BEETxoZ1fZQoaAZHQGXjQc5sCT5oB03oA2gIR0Crfok9Mbm2dX2UKGgGR0BuSsxKxs2vaAdNAgJoCEdAq4ElwPy08nV9lChoBkdAY4wml67dzmgHTegDaAhHQKuB4gQHzH11fZQoaAZHQGRAIwM6RyRoB03oA2gIR0Crh3iRfWtmdX2UKGgGR0BwRWDHwPRRaAdNUAFoCEdAq4e3M4cWCXV9lChoBkdAY/ZCNS619mgHTegDaAhHQKuL/VXFLnN1fZQoaAZHQGAQnqNZNfxoB03oA2gIR0CrjVpvP1L8dX2UKGgGR0BkmOu/1xsEaAdN6ANoCEdAq43Fq59Vm3V9lChoBkdAZBcPK+zt1WgHTegDaAhHQKuONgVoHs11fZQoaAZHQHCnjASFoL5oB03PAmgIR0CrkScC5mROdX2UKGgGR0BkfrpmmLtNaAdN6ANoCEdAq5Q7RYzSC3V9lChoBkdAZBCK3NLUTmgHTegDaAhHQKuUTlYEGJN1fZQoaAZHQGSS2/JvHcVoB03oA2gIR0CrlRA0TDfndX2UKGgGR0Bjk7IxQBPsaAdN6ANoCEdAq5UllyzXz3V9lChoBkdAPm7ofSx7iWgHS/5oCEdAq5rLHsC1Z3V9lChoBkdAZyo7FsHjZWgHTegDaAhHQKubA4n4O+Z1fZQoaAZHQGfGZxiobXJoB03oA2gIR0CrmxJx3mmtdWUu"
+    },
+    "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 sha256:388c769a0e322fd2667de7f5eed5007eb06a09ea1ca107a38382ce986445a270
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:84f42dc60093ee78889c62e30af016480445f106a3377df87fa507d85c2505c4
+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-5.15.120+-x86_64-with-glibc2.35 # 1 SMP Wed Aug 30 11:19:59 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": 239.7429835777062, "std_reward": 25.35671524203393, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-10-08T12:00:37.629084"}