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: 251.81 +/- 50.94
+      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 0x7f0d4b3fc4c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0d4b3fc550>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0d4b3fc5e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0d4b3fc670>", "_build": "<function ActorCriticPolicy._build at 0x7f0d4b3fc700>", "forward": "<function ActorCriticPolicy.forward at 0x7f0d4b3fc790>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0d4b3fc820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0d4b3fc8b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f0d4b3fc940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0d4b3fc9d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0d4b3fca60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0d4b3fcaf0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f0d4b38ea00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1730132227407595889, "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:": "gAWVPwwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQGFsi6pYLb6MAWyUTegDjAF0lEdAkKlNR3u/lHV9lChoBkdAZb8/Vy3kP2gHTegDaAhHQJCuoTewcHZ1fZQoaAZHQGJelSsKb8ZoB03oA2gIR0CQstr08NhFdX2UKGgGR0BkhkK5TZQIaAdN6ANoCEdAkLSql54W13V9lChoBkdAYOsCpWFN+WgHTegDaAhHQJC1SDyvs7d1fZQoaAZHQGOPa9sabWpoB03oA2gIR0CQtwTMqz7edX2UKGgGR0BheMj/uLJkaAdN6ANoCEdAkLuTrZ8KHHV9lChoBkdAYid3ta6jFmgHTegDaAhHQJC9N0IToMd1fZQoaAZHQGFiy8SPEKpoB03oA2gIR0CQvY7di2DydX2UKGgGR0BlTxUtI066aAdN6ANoCEdAkMCopYs/ZHV9lChoBkdAZvyQ+2VmjGgHTegDaAhHQJDAt0nw5Np1fZQoaAZHQGKszCDVYp5oB03oA2gIR0CQwppYcNpedX2UKGgGR0Bha2v0RODbaAdN6ANoCEdAkOfXX7Lt/nV9lChoBkdAZ1KOVgQYk2gHTegDaAhHQJDph+LFXJZ1fZQoaAZHQF3gDujRD1JoB03oA2gIR0CQ7itjCpFTdX2UKGgGR0BnJVBv73wkaAdN6ANoCEdAkPMJBPbfxnV9lChoBkdAZLIeOGTLXGgHTegDaAhHQJD2yXJHRTl1fZQoaAZHQGXwHsC1Z1VoB03oA2gIR0CQ/iG6PKdQdX2UKGgGR0Bh0DYoRZlnaAdN6ANoCEdAkQM4rJ8v3HV9lChoBkdAZMJSiM5wO2gHTegDaAhHQJEFEcjqv/11fZQoaAZHQGLhEaVD8cdoB03oA2gIR0CRBcLUkOZtdX2UKGgGR0BdzqrNnoPkaAdN6ANoCEdAkQduM6zVt3V9lChoBkdAYPNr0rbxmWgHTegDaAhHQJEMnXXiBGx1fZQoaAZHQGM7+JgsshBoB03oA2gIR0CRDoipvP1MdX2UKGgGR0Blv/egte2NaAdN6ANoCEdAkQ7rUgB91HV9lChoBkdAZo/mVZ9uxmgHTegDaAhHQJESXxqfvnd1fZQoaAZHQGc0FK02LpBoB03oA2gIR0CREm6cRUWEdX2UKGgGR0BfF1/pdKNAaAdN6ANoCEdAkRR8w+MZP3V9lChoBkdAUFFrFfiPyWgHS9toCEdAkRqKXOW0JHV9lChoBkdASsNFOO8012gHS9poCEdAkR4qOHWSU3V9lChoBkdAZJhu6VdHD2gHTegDaAhHQJEk85PuXu51fZQoaAZHQGCZJFb3XZpoB03oA2gIR0CROxiHqNZNdX2UKGgGR0BhADronrpraAdN6ANoCEdAkT//PcBU73V9lChoBkdAYIOVHnU2DWgHTegDaAhHQJFFCEtdzGR1fZQoaAZHQGJIR0dRziloB03oA2gIR0CRSKzdk8RudX2UKGgGR0BiTvMEA5q/aAdN6ANoCEdAkU4w8wHqvHV9lChoBkdAZNSsp5NXYGgHTegDaAhHQJFSRw97ngZ1fZQoaAZHQGUgWJBPbfxoB03oA2gIR0CRVBoGIKtxdX2UKGgGR0BkPlDv3JxOaAdN6ANoCEdAkVS0aMrEtXV9lChoBkdAZN12PDHfdmgHTegDaAhHQJFWYrWiDdx1fZQoaAZHQGAg9sBQvYhoB03oA2gIR0CRWy+QlruZdX2UKGgGR0BeA/3ztkWiaAdN6ANoCEdAkVz2bCrLhnV9lChoBkdAZWxDLKV6eGgHTegDaAhHQJFh9eLNwBJ1fZQoaAZHQGEhExIre69oB03oA2gIR0CRZT4gieNDdX2UKGgGR0BlMma2F36iaAdN6ANoCEdAkWzfwmVqvnV9lChoBkdAYpg6QvHtGGgHTegDaAhHQJFwy5PM0P91fZQoaAZHQGQP0Re1KGtoB03oA2gIR0CRd/Ju2qkudX2UKGgGR0Bj6hIxxkupaAdN6ANoCEdAkXm02LpA2XV9lChoBkdAYTHnRsuWbGgHTegDaAhHQJGQfjghr311fZQoaAZHQGZy5GKAJ9loB03oA2gIR0CRl0FNtZV5dX2UKGgGR0BivizollbvaAdN6ANoCEdAkZusGgSOBHV9lChoBkdAYS1wkxASnWgHTegDaAhHQJGgxHYpUgl1fZQoaAZHQGREk3CKrJdoB03oA2gIR0CRpNAcT8HfdX2UKGgGR0Biin863iJgaAdN6ANoCEdAkaalymygPHV9lChoBkdAYqVkYGdI5GgHTegDaAhHQJGnR3B55Z91fZQoaAZHQGCcMabWmP5oB03oA2gIR0CRqRBj4HopdX2UKGgGR0BghY3zcynDaAdN6ANoCEdAka4IiPhhpnV9lChoBkdAYGRuMuOCG2gHTegDaAhHQJGv1LEk0Jp1fZQoaAZHQGVEzcRDkU9oB03oA2gIR0CRs728IzFddX2UKGgGR0BlChhfBvaUaAdN6ANoCEdAkbXtOVPepHV9lChoBkdAZpDuP3i71GgHTegDaAhHQJG8nfWMCLd1fZQoaAZHQEvKbo8p1A9oB0vwaAhHQJG9eVv/BFd1fZQoaAZHQGDtAymALApoB03oA2gIR0CRwF4SpR4ydX2UKGgGR0BfXiimEXchaAdN6ANoCEdAkchZNsWO63V9lChoBkdAcScwmVqveWgHTRECaAhHQJHJXdZaFEl1fZQoaAZHQGDxDgZTAFhoB03oA2gIR0CRypguRLbpdX2UKGgGR0Bj0Fqk/KQraAdN6ANoCEdAkeDb52yLRHV9lChoBkdAYXD/Pw/gSGgHTegDaAhHQJHlZxgiNbV1fZQoaAZHQGbNJaaCtihoB03oA2gIR0CR6IZ4wAU+dX2UKGgGR0BO2AzguRLcaAdL42gIR0CR6MaufVZtdX2UKGgGR0BehjKLbYbsaAdN6ANoCEdAke065CngpHV9lChoBkdAYcHfgrH2iGgHTegDaAhHQJHwvQ5WBBl1fZQoaAZHQGa9c8kleGBoB03oA2gIR0CR8lGsFMZhdX2UKGgGR0Bi7Jxeb/fgaAdN6ANoCEdAkfLjEehf0HV9lChoBkdAYfjb9qDbrWgHTegDaAhHQJH7BcW0qpd1fZQoaAZHQGWkF9Brvb5oB03oA2gIR0CSAqcGkep5dX2UKGgGR0Bkk258Sf16aAdN6ANoCEdAkgUUug6EJ3V9lChoBkdAW5cIldC3PWgHTegDaAhHQJIL3RD1Gsp1fZQoaAZHQFz73ta6jFhoB03oA2gIR0CSDKpnHvMKdX2UKGgGR0BkGX8baRISaAdN6ANoCEdAkg8yHymQ83V9lChoBkdAXWoHt4RmLGgHTegDaAhHQJIVFgnc+JR1fZQoaAZHQGPib7Kq4pdoB03oA2gIR0CSFb5TqB3BdX2UKGgGR0BohpBLPD51aAdN6ANoCEdAkiz8I/qxDHV9lChoBkdAaIvfaYeDF2gHTegDaAhHQJIzZKJ2t+11fZQoaAZHQFJe4SYgJTloB0vqaAhHQJI2WaJAMUh1fZQoaAZHQGCGzYmLLp1oB03oA2gIR0CSN0NcGC7LdX2UKGgGR0BiulwrDqGDaAdN6ANoCEdAkjeJ6lchT3V9lChoBkdAZDTNO/L1VmgHTegDaAhHQJI8UHiWE9N1fZQoaAZHQGaujZlFtsNoB03oA2gIR0CSQFF72L5zdX2UKGgGR0Bj2kgr6LwXaAdN6ANoCEdAkkIdEsrd33V9lChoBkdAZv+jHGS6lWgHTegDaAhHQJJCxK28Zk11fZQoaAZHQGRWvsAvL5hoB03oA2gIR0CSScxoZhrndX2UKGgGR0BdLPkq+ajOaAdN6ANoCEdAklAxhDw6Q3V9lChoBkdAWITo1UEPlWgHTegDaAhHQJJSoYyfthN1fZQoaAZHQGaHlTWGyopoB03oA2gIR0CSWccjJMg2dX2UKGgGR0BkOASteUpvaAdN6ANoCEdAklqcXaakRHV9lChoBkdAYxIl2vB7/mgHTegDaAhHQJJdfqJMxoJ1fZQoaAZHQHH9RjawljVoB03uAmgIR0CSZGgTh5xBdX2UKGgGR0BkFvS2H+IeaAdN6ANoCEdAkmaLHlwLmnV9lChoBkdAaZJegte2NWgHTegDaAhHQJJsdi7TUiJ1ZS4="}, "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 Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.0+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "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 sha256:8cc412221829b8609a4ff912ae55e776bbf5546cc4500a9e517bc311673c72e3
+size 148016

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 0x7f0d4b3fc4c0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f0d4b3fc550>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f0d4b3fc5e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f0d4b3fc670>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f0d4b3fc700>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f0d4b3fc790>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f0d4b3fc820>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f0d4b3fc8b0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f0d4b3fc940>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f0d4b3fc9d0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f0d4b3fca60>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f0d4b3fcaf0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f0d4b38ea00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1730132227407595889,
+    "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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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1812d70a31ecb3c17a7aa9a2952fd1c31c614af5db46ef80d7046db738771975
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d74f7c64154002cbce329fcbbd942d1bb509c6f0298c0351f8e8784f589f19d0
+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 Thu Jun 27 21:05:47 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.5.0+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.0
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 251.8066696, "std_reward": 50.93876042767071, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-10-28T16:42:23.019050"}