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: 253.87 +/- 22.24
+      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 0x7f31eb5e6830>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f31eb5e68c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f31eb5e6950>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f31eb5e69e0>", "_build": "<function ActorCriticPolicy._build at 0x7f31eb5e6a70>", "forward": "<function ActorCriticPolicy.forward at 0x7f31eb5e6b00>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f31eb5e6b90>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f31eb5e6c20>", "_predict": "<function ActorCriticPolicy._predict at 0x7f31eb5e6cb0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f31eb5e6d40>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f31eb5e6dd0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f31eb5e6e60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f31eb58ad40>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1713780042759541645, "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:": "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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.2.1+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 sha256:175c97844e600365a31217da95cc926ab409540c49fa678f5ab6390485d0a972
+size 148064

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 0x7f31eb5e6830>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f31eb5e68c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f31eb5e6950>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f31eb5e69e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f31eb5e6a70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f31eb5e6b00>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f31eb5e6b90>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f31eb5e6c20>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f31eb5e6cb0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f31eb5e6d40>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f31eb5e6dd0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f31eb5e6e60>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f31eb58ad40>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1713780042759541645,
+    "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:": "gAWVMgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHEEHFo+OfeMAWyUTVYBjAF0lEdAl4D16JIlMXV9lChoBkdAcZwaoMrmQ2gHTQ8BaAhHQJeBBo9LYf51fZQoaAZHQG/f4etCAtpoB0v8aAhHQJeCK40/GER1fZQoaAZHQHEirDVH4GloB0vpaAhHQJeCStDD0lJ1fZQoaAZHQHClygGr0atoB02EAmgIR0CXgmxZMcp9dX2UKGgGR0Bzs28pTdcjaAdL6GgIR0CXgqRplBhQdX2UKGgGR0BuJ7mSyMUAaAdNRgFoCEdAl4NDT8YQ8XV9lChoBkdAbGSjynUDuGgHTSIBaAhHQJeDplqagEl1fZQoaAZHQHAkD6N2ki5oB00mAWgIR0CXg8hZyMkydX2UKGgGR0Bx8doQFs55aAdNHAFoCEdAl4TSwjdHlXV9lChoBkdAcIoHP/rB02gHTQsBaAhHQJeFMyN4qw11fZQoaAZHQG3TtEw35vdoB00gAWgIR0CXhdSamXPadX2UKGgGR0BsYTVBlcyFaAdNHwFoCEdAl4aVn/T9bXV9lChoBkdAckTXqJMxoWgHS+xoCEdAl4dvCyhSL3V9lChoBkdAcgIOE/Spi2gHTTgBaAhHQJeIy51/2Cd1fZQoaAZHQHICqufVZs9oB0vzaAhHQJeJYh+vyLB1fZQoaAZHQHDUL8BMi8poB00rAWgIR0CXicBF/hESdX2UKGgGR0BxQyYNRWLhaAdNKwFoCEdAl4nXe7+T/3V9lChoBkdAb+1oBaLXMGgHTTkBaAhHQJeKD0+TvAp1fZQoaAZHQG/TQM6RyOtoB00NAWgIR0CXiikqMFUydX2UKGgGR0BwaaVpsXSCaAdL/GgIR0CXiiuVopQUdX2UKGgGR0ByMPsByS3caAdNDgFoCEdAl4pwgLZzxXV9lChoBkdAcuBTt9hJAmgHS/xoCEdAl4sbvTgEU3V9lChoBkdAcQtqagElmmgHS+1oCEdAl4vsBMi8nXV9lChoBkdAcSytBfKISGgHTUYBaAhHQJeMym51/2F1fZQoaAZHQHMeY+KTB69oB00BAWgIR0CXjlQ+EAYIdX2UKGgGR0ByRPfuTibVaAdNRQFoCEdAl47u1a4c3nV9lChoBkdAcUyNMoMKC2gHTTEBaAhHQJePB3gUDdR1fZQoaAZHQG6Heq7yxzJoB00bAWgIR0CXj/UBnzxxdX2UKGgGR0ByiIQtjCpFaAdNEQFoCEdAl5D4SpR4yHV9lChoBkdAcJxnW8RL9WgHTQMBaAhHQJeRB5qubI91fZQoaAZHQHC/VuejEehoB0v4aAhHQJeRXrkbPyF1fZQoaAZHQHD91gUlAu9oB0v7aAhHQJeRXmeUY9B1fZQoaAZHQHHFWmYSg5BoB00IAWgIR0CXkhm51/2CdX2UKGgGR0BwRGB3A2ycaAdN9wFoCEdAl5Ko1xbSqnV9lChoBkdAcO98jzI3i2gHTT8BaAhHQJeTcwSJ0nx1fZQoaAZHQHHXGB4D9wZoB01jAWgIR0CXlD2NvOyFdX2UKGgGR0BxUy2NNrTIaAdNGQFoCEdAl5RW43FUAHV9lChoBkdAcCOwTdtVJmgHTTcBaAhHQJeUVooNNJx1fZQoaAZHQG75omgJ1JVoB00QAWgIR0CXlPP420iRdX2UKGgGR0ByJphfBvaUaAdNDAFoCEdAl5cRfOUt7XV9lChoBkdAcUbYlY2bX2gHTSIBaAhHQJeXs3CKrJd1fZQoaAZHQHAt6F23azxoB00EAWgIR0CXl9kYoAn2dX2UKGgGR0BtSIZwXIluaAdNGAFoCEdAl6wic9W6snV9lChoBkdAcSZDTz/ZNGgHTQ4BaAhHQJesLrqt5lh1fZQoaAZHQHJC0t7KJVNoB00bAWgIR0CXrL27FsHjdX2UKGgGR0BvzUafjCHiaAdNMAFoCEdAl60sXizcAXV9lChoBkdAcD4qx1PnCGgHTRYBaAhHQJetywSrYGt1fZQoaAZHQHFzdsabWmRoB00EAWgIR0CXrw6aLGaQdX2UKGgGR0BwiSRQrMC+aAdL92gIR0CXr63R5TqCdX2UKGgGR0Bte4N/e+EiaAdNyAFoCEdAl7A3WBjFynV9lChoBkdAcYaUutfXw2gHS/hoCEdAl7CMq4H5anV9lChoBkdAcOl9AX2ugmgHTRsBaAhHQJew9MEidJ91fZQoaAZHQHGbESh8IAxoB01iAWgIR0CXsW/jsD4hdX2UKGgGR0BxH14SpR4yaAdNMwFoCEdAl7GqU/wAl3V9lChoBkdAcFr/jsD4g2gHTR8BaAhHQJezsVARkEt1fZQoaAZHQHNZHVTaTOhoB00WAWgIR0CXs/0VafSQdX2UKGgGR0BzNWG7BfrsaAdNFwFoCEdAl7Qk+xGDtnV9lChoBkdAb3KTK1XvIGgHS/1oCEdAl7TzIFNcnnV9lChoBkdAcB3a/RE4N2gHTQcBaAhHQJe2DY7JW/91fZQoaAZHQHHBTEzfrKNoB00FAWgIR0CXtnEQGwA3dX2UKGgGR0BtAsCgbp/xaAdNHwFoCEdAl7aY6nzg/HV9lChoBkdAbtY54GD+SGgHTTABaAhHQJe2ulsP8Q91fZQoaAZHQHMF6vmozepoB00NAWgIR0CXt4QtSQ5ndX2UKGgGR0Bt8M8DB/I9aAdNFwFoCEdAl7g79l2/z3V9lChoBkdAcE5VqesgdWgHTQsBaAhHQJe4naWX1J11fZQoaAZHQG/X1r6+FlFoB00cAWgIR0CXuMinYQJ5dX2UKGgGR0BvevHtF8XvaAdNGQFoCEdAl7laJZW7v3V9lChoBkdAcKdb1yvLYGgHTQwBaAhHQJe5sCaJAMV1fZQoaAZHQGI9syrPt2NoB03oA2gIR0CXu3p9qk/KdX2UKGgGR0BwmJ+iJwbVaAdNWQFoCEdAl7ub6UJOWXV9lChoBkdAbA4vsZ5zHWgHTQ4BaAhHQJe8Bswco6V1fZQoaAZHQHKBVnEl3QloB00WAWgIR0CXvGZTAFgVdX2UKGgGR0BwuYvBacI7aAdNMwFoCEdAl7zKBd2Pk3V9lChoBkdARKekHlfZ3GgHTQEBaAhHQJe9itga3ql1fZQoaAZHQHBJaaLGaQVoB0v6aAhHQJe90sRQJol1fZQoaAZHQGwYzJ6po9NoB00JAWgIR0CXviQYDTz/dX2UKGgGR0Bw+D2Jzkp7aAdNPQFoCEdAl75ndfsu4HV9lChoBkdAc2q7rs0HhWgHTRYBaAhHQJe+vwAlv611fZQoaAZHQHHH9wWFev9oB0v3aAhHQJe/O1+iJwd1fZQoaAZHQGzlwEyLyc1oB0v6aAhHQJe/pTfixV11fZQoaAZHQHI5rXHzYmNoB00sAWgIR0CXwT0gbIcSdX2UKGgGR0BvLF1+y7f6aAdNFgFoCEdAl8E/AwfyPXV9lChoBkdAbT+M0gr6L2gHTV8BaAhHQJfBhxVAAyV1fZQoaAZHQG24NipeeFtoB00hAWgIR0CXwe5HmRvFdX2UKGgGR0BwQQ9jgAIZaAdNKQFoCEdAl8Qgw9JSSHV9lChoBkdAcZ6IiTt9hWgHTSgBaAhHQJfEQMEzO5d1fZQoaAZHQHDs1lXiiqRoB00eAWgIR0CXxNhc7hegdX2UKGgGR0Bu9oEIPbwjaAdNMQFoCEdAl8UG/Firk3V9lChoBkdAcNrVn27FsGgHS/doCEdAl8VuhGpdbHV9lChoBkdAcNySWZ7Xx2gHTQcBaAhHQJfFmyeI2wV1fZQoaAZHQG/Au938n/loB01QAWgIR0CXxtBnjABUdX2UKGgGR0BuEsPQOWjXaAdNNgFoCEdAl8bMAq/dqXV9lChoBkdAccbRVZLZjGgHTQMBaAhHQJfHBXV9Wp91fZQoaAZHQHKybzGxUvRoB002AWgIR0CXx4t4RmK7dX2UKGgGR0Bx/NVdX1aoaAdNGgFoCEdAl8gMNtqHoHV9lChoBkdAcWX3t8eCCmgHS/hoCEdAl8jfRZ2ZA3V9lChoBkdAceMkTpPhymgHTWcBaAhHQJfJOKR+z+p1fZQoaAZHQHGa9z4k/r1oB00tAWgIR0CXyhsZpBX0dX2UKGgGR0BywHhXKbKBaAdNQwFoCEdAl8qxZpztC3VlLg=="
+    },
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f58fc7c9fd4185a69908d3e1b1f8b5c4f01948e6b3f22d033f6c2812a3d6857f
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:72afb310af0abf6a01cfd9915e74fe605823edbe8b80d83dfb407f7268e2a97e
+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-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.2.1+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": 253.8701896816911, "std_reward": 22.24186795109643, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-04-22T10:28:32.506614"}