Upload PPO LunarLander-v2 trained agent

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

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: 264.24 +/- 19.94
+      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 0x7ff4fc003f60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff4fc004040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff4fc0040e0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff4fc004180>", "_build": "<function ActorCriticPolicy._build at 0x7ff4fc004220>", "forward": "<function ActorCriticPolicy.forward at 0x7ff4fc0042c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff4fc004360>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff4fc004400>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff4fc0044a0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff4fc004540>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff4fc0045e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff4fc004680>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff4fc16bcc0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1743435226639672190, "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:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="}, "_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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "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": "3.11.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.6.0+cu124", "GPU Enabled": "True", "Numpy": "2.0.2", "Cloudpickle": "3.1.1", "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:b0d6a55ec5656aed48ffdeb9a875fd005663a445c12092c5925922136bd1aaaf
+size 148123

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 0x7ff4fc003f60>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff4fc004040>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff4fc0040e0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff4fc004180>",
+        "_build": "<function ActorCriticPolicy._build at 0x7ff4fc004220>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7ff4fc0042c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff4fc004360>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff4fc004400>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7ff4fc0044a0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff4fc004540>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff4fc0045e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff4fc004680>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7ff4fc16bcc0>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1743435226639672190,
+    "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:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="
+    },
+    "_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:": "gAWVPAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQDPHmjj7yhCMAWyUS/uMAXSUR0CeGOKVY6n0dX2UKGgGR0BIzyZSeiBYaAdL5mgIR0CeGh00WM0hdX2UKGgGR0Bk8ZPVNHpbaAdN6ANoCEdAnh1pnxri2nV9lChoBkdAYclx+az/qGgHTegDaAhHQJ4erezlcQl1fZQoaAZHQGVU1Oj7AL1oB03oA2gIR0CeIH9FF2FGdX2UKGgGR0Bj52v8qFyraAdN6ANoCEdAniFHoHLRr3V9lChoBkdAPyTSG8EmpmgHTRsBaAhHQJ4mwrUb1h91fZQoaAZHQFmy1ie/YapoB03oA2gIR0CeKx62OQyRdX2UKGgGR0Bdf/8yeqaPaAdN6ANoCEdAnj88+RoysXV9lChoBkdAXmpWS2Yv4GgHTegDaAhHQJ5B3+6y0KJ1fZQoaAZHQGR/d0Rvm5loB03oA2gIR0CeROlHz6JqdX2UKGgGR0Bj4G8f3evZaAdN6ANoCEdAnkj6PCEYfnV9lChoBkdAYmTYsd1dPmgHTegDaAhHQJ5JMtFrl/91fZQoaAZHQGkE003wTdtoB03oA2gIR0CeVKdUKiPAdX2UKGgGR0AhN/wy6+WXaAdL8GgIR0CeWo6YE4ecdX2UKGgGR0BiDXddmg8KaAdN6ANoCEdAnls7vkRzzXV9lChoBkdAYsbiobXHzmgHTegDaAhHQJ5gZT6zmfZ1fZQoaAZHQGZRvT5O8ChoB03oA2gIR0CeameFL39KdX2UKGgGR0Bja32PDHfeaAdN6ANoCEdAnmu2PYFqz3V9lChoBkdAYgekl/pdKWgHTegDaAhHQJ5uBtdiUgV1fZQoaAZHQF/1HuZ1FH9oB03oA2gIR0Cebtr9VFQVdX2UKGgGR0Au0AH3UQTVaAdL6WgIR0Cebt39rGipdX2UKGgGR0BinrXe3x4IaAdN6ANoCEdAnnASBPKuCHV9lChoBkdAYC+FwkxASmgHTegDaAhHQJ5wjfgrH2h1fZQoaAZHQF5K5C4SYgJoB03oA2gIR0CedYbI91U3dX2UKGgGR0BfXnqRlpXZaAdN6ANoCEdAnnm4VRDTjXV9lChoBkdAYos33pOermgHTegDaAhHQJ6P57/n4fx1fZQoaAZHQGWCBiTdLxtoB03oA2gIR0CekrA1vVEvdX2UKGgGR0BjholKK509aAdN6ANoCEdAnpW/oJRfnnV9lChoBkdAYX5+/gzguWgHTegDaAhHQJ6aG1jRUm51fZQoaAZHQGHjMPBi1AtoB03oA2gIR0Ceo4Kxs2vTdX2UKGgGR0BilIcR15jZaAdN6ANoCEdAnqmFbaAWi3V9lChoBkdAYu/YDklu32gHTegDaAhHQJ6qL41xbSt1fZQoaAZHQDWFY0VJtixoB003AWgIR0CeukeP7vXtdX2UKGgGR0BlD/27FsHjaAdN6ANoCEdAnrwAmu1WsHV9lChoBkdAYEjhoduHe2gHTegDaAhHQJ69U2sJY1Z1fZQoaAZHQF/uPFvQ4S9oB03oA2gIR0Cev+JQcghbdX2UKGgGR0BmBZMYdhiLaAdN6ANoCEdAnsDhpQDV6XV9lChoBkdAY+9F4LThHmgHTegDaAhHQJ7A5cC5mRN1fZQoaAZHQGSKgjIJZ4hoB03oA2gIR0CewkHHmzSkdX2UKGgGR0BjIWS6lLvkaAdN6ANoCEdAnsLJ2ECeVnV9lChoBkdAZJb37DVH4GgHTegDaAhHQJ7IatMfzSV1fZQoaAZHQCMU1baAWi1oB0vPaAhHQJ7MQ0dilSF1fZQoaAZHQGNHQLux8lZoB03oA2gIR0CezVMpgCwKdX2UKGgGR0BkHkhib2DhaAdN6ANoCEdAns9i6tknTnV9lChoBkdAYYVGUfPom2gHTegDaAhHQJ7mRrrPdEd1fZQoaAZHQGFgp7kXDWNoB03oA2gIR0Ce6VCo0hvBdX2UKGgGR0BisCjafzz3aAdN6ANoCEdAnu1a7dznzXV9lChoBkdASD4JE6T4cmgHTTkBaAhHQJ7yHKdQO4J1fZQoaAZHQDnBZV4oqkNoB0vuaAhHQJ73tHrhR651fZQoaAZHQF9CkZ75VOtoB03oA2gIR0Ce/G2+fywwdX2UKGgGR0BjllB0IToMaAdN6ANoCEdAnv0V6qsEJXV9lChoBkdAYijWLgn+h2gHTegDaAhHQJ8K77Q9ic51fZQoaAZHQGb+sZxaPjpoB03oA2gIR0CfDUrNW2gGdX2UKGgGR0BmBKZhKDkEaAdN6ANoCEdAnw8VpoK2KHV9lChoBkdAYmd5hz/6wmgHTegDaAhHQJ8S1P9DQZ51fZQoaAZHQGM2MkyDZlFoB03oA2gIR0CfE/89wFTvdX2UKGgGR0Bg/0lPacqfaAdN6ANoCEdAnxWfCQ9zO3V9lChoBkdAYyvPN3W4E2gHTegDaAhHQJ8WNtix3V11fZQoaAZHQGSxInjQzDZoB03oA2gIR0CfHHw6QvHtdX2UKGgGR0BQlSMxXXAeaAdL7GgIR0CfHv0xdpqRdX2UKGgGR0BjZK6vq1PWaAdN6ANoCEdAnyHVlsguAnV9lChoBkdAYzyJemelK2gHTegDaAhHQJ8kERbr1NB1fZQoaAZHQGLXQOvt+kRoB03oA2gIR0CfOSnFHavidX2UKGgGR0Be4Pd69kBkaAdN6ANoCEdAn0PgZwXIl3V9lChoBkdAYJDaRISUT2gHTegDaAhHQJ9JBMj/uLJ1fZQoaAZHQGSNzhgmZ3NoB03oA2gIR0CfToGqxTsIdX2UKGgGR0BfAoqbz9S/aAdN6ANoCEdAn1L4FA3T/nV9lChoBkdAYBhe8f3evmgHTegDaAhHQJ9TjuMMqjJ1fZQoaAZHQGRrIbOu7pVoB03oA2gIR0CfYDVzp5eJdX2UKGgGR0Bg3XwEyLydaAdN6ANoCEdAn2GyjQAuI3V9lChoBkdAYHSRaouPFWgHTegDaAhHQJ9lJ36hxo91fZQoaAZHQGQMgzxgAp9oB03oA2gIR0CfZgs+FDfFdX2UKGgGR0BbX+evpyIYaAdN6ANoCEdAn2dSQkona3V9lChoBkdAYOytRvWH12gHTegDaAhHQJ9n7GACnxd1fZQoaAZHQGExXDm8ujBoB03oA2gIR0CfbfMI/qxDdX2UKGgGR0BBGKSgXdj5aAdL82gIR0Cfbihxo7FLdX2UKGgGR0BjSjfixVyWaAdN6ANoCEdAn3CunZTQ3XV9lChoBkdAYt9NA1Nxl2gHTegDaAhHQJ9zxusLfDV1fZQoaAZHQGIY31zySV5oB03oA2gIR0CfdYFB6a9cdX2UKGgGR0BjgChSLqD9aAdN6ANoCEdAn3fwJ9iMHnV9lChoBkfAEKAIppeu3mgHTRUBaAhHQJ938q+ajN91fZQoaAZHQGNnqk2xY7toB03oA2gIR0CfkO7fYSQHdX2UKGgGR0Bgs6XUpd8iaAdN6ANoCEdAn5VlmnO0LXV9lChoBkdAQ1oVZcLSeGgHTQcBaAhHQJ+VrY287IV1fZQoaAZHQGK4XhfjS5RoB03oA2gIR0CfmltXPqs2dX2UKGgGR0BdYhs67ulXaAdN6ANoCEdAn5/cMmWt2nV9lChoBkdAY+x7VJ+UhWgHTegDaAhHQJ+go/iYLLJ1fZQoaAZHQEXtMsYl6Z9oB00DAWgIR0CfpzG5+YtydX2UKGgGR0BjU4J1JUYLaAdN6ANoCEdAn7Cqcy31BnV9lChoBkdAWHSU0Nz8xmgHTegDaAhHQJ+051fVqet1fZQoaAZHQGS9BaLXL/1oB03oA2gIR0CftgIbfgrIdX2UKGgGR0Bhu4BcRlH0aAdN6ANoCEdAn7eGoR7JGXV9lChoBkdAY8CTwDvE0mgHTegDaAhHQJ++/82rGR51fZQoaAZHQF85uBczImxoB03oA2gIR0CfvzMINVindX2UKGgGR0BkLkj9n9NvaAdN6ANoCEdAn8F8VpKzzHV9lChoBkdAY4ipWmxdIGgHTegDaAhHQJ/EIkMTewd1fZQoaAZHQFP/E3Kji4toB00QAWgIR0CfyFms/6frdX2UKGgGR0Bn2I2uPmxMaAdN6ANoCEdAn8k/FirksHV9lChoBkdAYtL+YtxuK2gHTegDaAhHQJ/JQYUFjd51ZS4="
+    },
+    "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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu",
+        "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 sha256:70531f65a94d3b7b8a17b20331ddb77a722790b19442840920bcba3309ecc94a
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c80dbb9e0b625d5d1a4fd37dde2436615fd851ea0fd9bbb860916eb363efbff3
+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: 3.11.11
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.6.0+cu124
+- GPU Enabled: True
+- Numpy: 2.0.2
+- Cloudpickle: 3.1.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ab95686d849468b9975071eee294ac6f6002c535d2af7585c3f628cb9f3f7a13
+size 166284

results.json

@@ -0,0 +1 @@
+{"mean_reward": 264.24462275115667, "std_reward": 19.93680325288539, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-03-31T16:20:10.293612"}