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: 236.34 +/- 45.92
+      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 0x7fbbf903ef80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbbf903f010>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbbf903f0a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbbf903f130>", "_build": "<function ActorCriticPolicy._build at 0x7fbbf903f1c0>", "forward": "<function ActorCriticPolicy.forward at 0x7fbbf903f250>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbbf903f2e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbbf903f370>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbbf903f400>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbbf903f490>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbbf903f520>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbbf903f5b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fbbf903a600>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1688697420970688278, "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:": "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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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.0.1+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "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:f521704c3e5a1fb2f09b06150902f58d500eb2acd9a6fd72d4c1d477e2dae17b
+size 146735

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 0x7fbbf903ef80>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbbf903f010>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbbf903f0a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbbf903f130>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbbf903f1c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbbf903f250>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbbf903f2e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbbf903f370>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbbf903f400>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbbf903f490>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbbf903f520>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbbf903f5b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fbbf903a600>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1688697420970688278,
+    "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:": "gAWVMgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHBBaMzdk8SMAWyUTW8BjAF0lEdAlyKxFEy+H3V9lChoBkdAYaG1zhgmZ2gHTegDaAhHQJci3+rELpl1fZQoaAZHQCki+N96TntoB0vQaAhHQJckKaKDTSd1fZQoaAZHQHJN0l3Qla9oB00NAWgIR0CXJESowVTKdX2UKGgGR0BxI9xjriVCaAdNRgFoCEdAlyRzqKP4mHV9lChoBkdAcwPnzg/C7GgHTakBaAhHQJclPjU/fO51fZQoaAZHQHDO8Wj4595oB00fAWgIR0CXJWYJVsDXdX2UKGgGR0BwxbTnaFmGaAdNHAFoCEdAlyV3i704BHV9lChoBkdARtjHKfWc0GgHS+JoCEdAlyYqd1+y7nV9lChoBkdAYX7LzPKMemgHTegDaAhHQJcnWWa+evp1fZQoaAZHQETVrN4Z/CtoB0vnaAhHQJcpNOARTS91fZQoaAZHQFC5Nucc2itoB0vUaAhHQJcq+jQAuI11fZQoaAZHQHCiEtI065poB00jAWgIR0CXKylP8AJcdX2UKGgGR0Bux5iw0O3EaAdNOQFoCEdAlytmITGo73V9lChoBkdAcljiG34KyGgHTRkBaAhHQJcsmVnmJWN1fZQoaAZHQEYWpyZKFqVoB0u8aAhHQJcsmUQkHD91fZQoaAZHQG8GaNEPUa1oB00iAWgIR0CXLRbLU1AJdX2UKGgGR0ByfQIF/x2CaAdNKQFoCEdAly6/jn3cpXV9lChoBkdAcEbevZAY52gHTToBaAhHQJcu5/kNnXd1fZQoaAZHQHDL57w8W9FoB00gAWgIR0CXL8hLoOhCdX2UKGgGR0Byxumbb1yvaAdNKgFoCEdAlzAbCBPKuHV9lChoBkdAcS5/echC+mgHTRwBaAhHQJcw6ygPEsJ1fZQoaAZHQHBh3TNMXadoB00GAWgIR0CXMO1+RYA9dX2UKGgGR0ByFdgssg+yaAdNUQFoCEdAlzHoUrTYunV9lChoBkdATreU8mrsB2gHS7ZoCEdAlzKIc/+sHXV9lChoBkdAcdHRD1Gsm2gHTRwBaAhHQJcy/s1KoQ51fZQoaAZHQHA3Zw84gihoB0vzaAhHQJczUSGrS3N1fZQoaAZHQHAUHVkMCtBoB01iAWgIR0CXM3VmSQo1dX2UKGgGR0Bt1S/Zdv87aAdNBgFoCEdAlzWsN+b3GnV9lChoBkdAcWrcWj4592gHTSoBaAhHQJc29UbT+eh1fZQoaAZHQHC5iKWLP2RoB00LAWgIR0CXNxSAYpDvdX2UKGgGR0BPLFk6Lfk4aAdLxWgIR0CXNztAs053dX2UKGgGR0Bwy13Tuv2XaAdNCgFoCEdAlzeDINmUW3V9lChoBkdAcUtmhdt2tGgHTRgBaAhHQJc3km/nGKh1fZQoaAZHQHGpR2jfvWpoB0vzaAhHQJc4OJN0vGp1fZQoaAZHQA2r7wazeGhoB0vaaAhHQJc4+vV3EAJ1fZQoaAZHQFIr6C17Y05oB00NAWgIR0CXOiHkLhJidX2UKGgGR0Bw3/OpsGgSaAdNPgFoCEdAlzrZqh11XHV9lChoBke/tahHskY4yWgHS9ZoCEdAlzuB2B8QZnV9lChoBkdAcSvMBIWgvmgHTUABaAhHQJc9DsXzlLh1fZQoaAZHQG6v08vEjxFoB00zAWgIR0CXPZRnvlU7dX2UKGgGR0Bx7C938n/laAdNIAFoCEdAlz5c3Q2MsHV9lChoBkdAcLUgLZzxPWgHTTgBaAhHQJc+fLlmvnt1fZQoaAZHQG9QIuwosqdoB005AWgIR0CXPwJyhi9adX2UKGgGR0BxAKFg2IfsaAdNCQFoCEdAl0AkwWWQfnV9lChoBkdAUrTE5yU9p2gHS9poCEdAl0DZbD/EO3V9lChoBkdAcX1u1WsBAGgHTQIBaAhHQJdBFyaNMoN1fZQoaAZHQHB1v2Xb/OtoB00JAWgIR0CXQXidJ8OTdX2UKGgGR0BwGlcLSeAeaAdL+2gIR0CXQWz9jwx4dX2UKGgGR0BwVhs0pEx7aAdNDwFoCEdAl0I+irT6SHV9lChoBkdAbyoL0jC53GgHTRkBaAhHQJdCbYqXnhd1fZQoaAZHQHHKy4nWrfdoB00HAWgIR0CXX2by6MBIdX2UKGgGR0BOJV32VVxTaAdL7mgIR0CXX5cSXdCWdX2UKGgGR0Bugkuez2OAaAdNRQFoCEdAl2OXwXqJM3V9lChoBkdAceUFQ2uPm2gHTSQBaAhHQJdkbwe/5+J1fZQoaAZHQHB25wXIlt1oB01aAWgIR0CXZSf1YhdMdX2UKGgGR0ByGkFs54nnaAdNGAFoCEdAl2VWGdqcmXV9lChoBkdAclCYxL0z02gHTTEBaAhHQJdnKbExZdR1fZQoaAZHQHMrDO1OTJRoB01dAWgIR0CXZ4OFQEZBdX2UKGgGR0Byc+AlOXVtaAdNSAFoCEdAl2eSCrcTJ3V9lChoBkdAcwOjin5zo2gHTSMBaAhHQJdoyYa5wwV1fZQoaAZHQHDqHj2i+L5oB00HAWgIR0CXaOJK8L8adX2UKGgGR0BzhbL6k691aAdNFwFoCEdAl2ljySV4YHV9lChoBkdAcepaDPGACmgHTT0BaAhHQJdpmCe2/i51fZQoaAZHQHHftAPd2xJoB00wAWgIR0CXaakHD766dX2UKGgGR0BxPbQzDXOGaAdNVAFoCEdAl2nLBGhEjXV9lChoBkdAcNkMB6rvLGgHTUQBaAhHQJdqWu8scyZ1fZQoaAZHQEGgClJpWWBoB0vdaAhHQJdsSmZVn291fZQoaAZHQHEyqK+BYmtoB01cAWgIR0CXbO1F6RhddX2UKGgGR0BwoVhkRSP2aAdNWAFoCEdAl2z/m9xp+XV9lChoBkdAbS2pQUHpr2gHTRoBaAhHQJdv+cvugHx1fZQoaAZHQHC/3dfsu4BoB00zAWgIR0CXcGKGcnVodX2UKGgGR0BwwrQQcxTLaAdL/WgIR0CXcLSlnAZbdX2UKGgGR0BwrHn9vS+haAdNOwFoCEdAl3GYZEUj9nV9lChoBkdAblRR3u/lAGgHTQEBaAhHQJdzJBt1p0x1fZQoaAZHQHBz8vAXVLBoB00wAWgIR0CXc0VZ9uxbdX2UKGgGR0Bv5dcry1/laAdNNwFoCEdAl3OccdYGMXV9lChoBkdAbUEu+RHPNWgHTR8BaAhHQJdzw/qxC6Z1fZQoaAZHQHImnZoPCl9oB00QAWgIR0CXdNtALRa5dX2UKGgGR0BwPyZ2IO6NaAdNRAFoCEdAl3Va2fChvnV9lChoBkdAb88vYe1a4mgHTS8BaAhHQJd1c2dd3St1fZQoaAZHQHBCop+c6NloB007AWgIR0CXdbNKRMewdX2UKGgGR0BwTYQvpQk5aAdNOgFoCEdAl3W2I9C/oXV9lChoBkdAclDoDgZTAGgHTTQBaAhHQJd4VOZb6gx1fZQoaAZHQHABed07r9loB01AAWgIR0CXeXk3CKrJdX2UKGgGR0BLtf8EV32VaAdL7GgIR0CXeug62fCidX2UKGgGR0Bv9RTAFgUlaAdNawFoCEdAl3tgeFL39XV9lChoBkdAcDeVh1DBuWgHTSEBaAhHQJd7+35N47l1fZQoaAZHQHJ9b3K0UoNoB00uAWgIR0CXfCfjS5RTdX2UKGgGR0BwB3zoUzsQaAdNJQFoCEdAl3xziwSrYHV9lChoBkdAbIdTF2mpEWgHS/1oCEdAl3232M85j3V9lChoBkdAcNCIk7fYSWgHTRcBaAhHQJd+5TsIE8t1fZQoaAZHQHCvPIjnmq5oB00lAWgIR0CXf0HLRrrPdX2UKGgGR0BuXS/ub7TEaAdNFwFoCEdAl4DCPU8V6HV9lChoBkdAbYV8XN1QqWgHTSIBaAhHQJeCBDzAeq91fZQoaAZHQHGAKjSG8EpoB01lAWgIR0CXgrCQtBfKdX2UKGgGR0BwjDj3mFJyaAdNJwFoCEdAl4LFII4VAXV9lChoBkdAbucNe+mFamgHTTQBaAhHQJeDeBNEgGN1fZQoaAZHQG6z109yLhtoB01VAWgIR0CXhMRqGlANdX2UKGgGR0BsN0Re1KGtaAdNOgFoCEdAl4gCjHn2ZnVlLg=="
+    },
+    "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 sha256:5d27fb1e08fb153f1f71cabb180c33b518e4942db298d741ecbb4e0438a05d95
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:63b549b1c983d7125cc2f375b423d9c8d057ea2476dc295f54b71f39554a4139
+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-5.15.107+-x86_64-with-glibc2.31 # 1 SMP Sat Apr 29 09:15:28 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.0.1+cu118
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 236.34390833966148, "std_reward": 45.92433272621777, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-07-07T03:06:49.264334"}