First upload (Deep RL course U1)

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: -121.24 +/- 13.26
+      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 0x7c85d2bdecb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c85d2bded40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c85d2bdedd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c85d2bdee60>", "_build": "<function ActorCriticPolicy._build at 0x7c85d2bdeef0>", "forward": "<function ActorCriticPolicy.forward at 0x7c85d2bdef80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c85d2bdf010>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c85d2bdf0a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7c85d2bdf130>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c85d2bdf1c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c85d2bdf250>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c85d2bdf2e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7c85d2b88200>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 229376, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1723557604271794153, "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": -0.1468799999999999, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWV8wsAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHwEvQrsjVx0eMAWyUS6iMAXSUR0CEA5zg/C66dX2UKGgGR8AgtoVVPva2aAdLdmgIR0CEB157gKnfdX2UKGgGR8AqKji4rjHXaAdL5WgIR0CEEe0ygwoLdX2UKGgGRz/zljRUm2LHaAdLZ2gIR0CEEeyRjjJddX2UKGgGR0A+NZTQ3PzGaAdLsWgIR0CEFQ3S8an8dX2UKGgGR8BSO4G2TgVHaAdL7mgIR0CEFYwFkhA4dX2UKGgGR8BmcVB0IToMaAdNEwFoCEdAhBfhEjPfK3V9lChoBkfASWFX1anrIGgHS6xoCEdAhBhWQnx8UnV9lChoBkfAJx90JWvKU2gHS8loCEdAhBhZIg/1QXV9lChoBkfASw0gU1yeZ2gHTR0BaAhHQIQZF9hJAdJ1fZQoaAZHwD5uuGKyfL9oB0vnaAhHQIQbqWszVMF1fZQoaAZHwGmIyDIzWPNoB02AAmgIR0CEI+6VdHDrdX2UKGgGR0AUcuJ1q33IaAdLx2gIR0CEJStNBWxRdX2UKGgGR0BIn5h8YyfuaAdLhWgIR0CEK8ruIAOsdX2UKGgGR0BO5Y5T6zmfaAdN6ANoCEdAhC9kQf6oEXV9lChoBkdALAs+eOGTLWgHS8toCEdAhDUtXgccVHV9lChoBkfAUE99a2WpqGgHS7doCEdAhDZm5DqnnHV9lChoBkfATPSAz544ZWgHS/ZoCEdAhDn+IdlunHV9lChoBkfASW5mPHT7VWgHS9xoCEdAhD03DFZPmHV9lChoBkfANYqAOJ+DvmgHS9loCEdAhEFIMSbpeXV9lChoBkc/91L127nPmmgHS2VoCEdAhEShYNiH7HV9lChoBkdATrcUoKD02GgHTegDaAhHQIRFxaV2Rq51fZQoaAZHwCyUI9kjHGVoB0u5aAhHQIRHvZqVQhx1fZQoaAZHwFKM/rSmZVpoB00hAWgIR0CER/NTLns+dX2UKGgGR8BFnfDk2gnMaAdLx2gIR0CESKR6nivQdX2UKGgGR0A5mKyv9tMxaAdLl2gIR0CESahoM8YAdX2UKGgGR8BTop5mh/RWaAdNHQFoCEdAhEpgf2bobHV9lChoBkdAOKhgqmTC+GgHS25oCEdAhExldLQHA3V9lChoBkfAPoQam4y44WgHS5VoCEdAhFHf20zCUHV9lChoBkdAP9hRZU1hs2gHS4JoCEdAhFlWQfZElXV9lChoBkfATr0TBZZB9mgHS6hoCEdAhFnvUaya/nV9lChoBkdAQ2omw7kn1GgHS4VoCEdAhFqHUMG5c3V9lChoBkfAQgVnVXmvGWgHS6doCEdAhFyL9uP3jHV9lChoBkdAVA9RKpT/AGgHTegDaAhHQIRcjL6k6911fZQoaAZHQDncKLKmsNloB03oA2gIR0CEXPy5qdpZdX2UKGgGR0BXSDujRD1HaAdN6ANoCEdAhF1hi1Aqu3V9lChoBkdAQM33SKFZgWgHS+9oCEdAhF90ZNwiq3V9lChoBkfAPbtVmz0HyGgHS6ZoCEdAhF/wOnVG1HV9lChoBkfAUPUysS00FmgHS79oCEdAhGC2JSBK+XV9lChoBkfALus23rleW2gHS7poCEdAhGIvYWcjJXV9lChoBkfAV3zhBJI1+GgHS+JoCEdAhGML/0dzXHV9lChoBkfAUgLUVi4J/2gHS91oCEdAhGNhJqZc9nV9lChoBkfAMRaQ/5ckdGgHS8hoCEdAhGVcslLOA3V9lChoBkdAEkOEug6EJ2gHS5doCEdAhGeokAxSHnV9lChoBkdANkIIF/x2CGgHS7NoCEdAhGgfPPcBVHV9lChoBkfAVWlKf4AS4GgHS7hoCEdAhGg0dBBzFXV9lChoBkfAM6xFRYRuj2gHS7NoCEdAhGmO/UONHnV9lChoBkfAR6O4wyqMnGgHS7loCEdAhGn2THKfWnV9lChoBkfANaPv4M4LkWgHS9ZoCEdAhGsG+9Jz1nV9lChoBkfAOj3trsSkCWgHS6poCEdAhGwTIvJzUHV9lChoBkfALCr3sXzlLmgHS7poCEdAhGzIIOYplXV9lChoBkfAVSkyBTXJ5mgHS3FoCEdAhG3XRw6ySnV9lChoBkdAM545PuXu3WgHS6hoCEdAhG6bHp8neHV9lChoBkc/7YRdyDIzWWgHS6RoCEdAhG9Rvm5lOHV9lChoBkdAQBiASWZ7X2gHS/ZoCEdAhG9Q1BMSK3V9lChoBkdAEeWSU1Q662gHS3xoCEdAhHOtdJJ5FHV9lChoBkfASFxR/EwWWWgHS3RoCEdAhHRLmyPdVXV9lChoBkfAWGF2xIJ7cGgHTRQBaAhHQIR50c2itaJ1fZQoaAZHwEzRvttygf5oB0vWaAhHQIR8AMF2V3V1fZQoaAZHv+baUzKs+3ZoB0u0aAhHQIR9FBQemvZ1fZQoaAZHv7X3+MqBmPJoB0ukaAhHQIR+taQmu1Z1fZQoaAZHQDIwH/tICltoB00LAWgIR0CEfzmig00ndX2UKGgGR8BkjbZ39rGjaAdNnwNoCEdAhIXWN3np0XV9lChoBkfAUgryhBZ6lmgHS+5oCEdAhIczIV/MGHV9lChoBkfAIRq3d9Dx9WgHTQMBaAhHQISIvFaSs8x1fZQoaAZHwD35nWattANoB0veaAhHQISLhNATqSp1fZQoaAZHQD3vx6OYIB1oB0uZaAhHQISNKs2eg+R1fZQoaAZHwGL+H0btJFtoB012AWgIR0CEkkfU4JeFdX2UKGgGR8BAfA6ltTDPaAdLtWgIR0CEk4G1QZXNdX2UKGgGR8Au277Kq4pdaAdLbGgIR0CEk6QAdXDFdX2UKGgGR8BK1eVcD8tPaAdLjWgIR0CElj79AHE/dX2UKGgGR8A2bFi8WbgCaAdLiWgIR0CEmRsbedkKdX2UKGgGR8Bc9NCJGe+VaAdNEwFoCEdAhJnFPrOZ9nV9lChoBkfAKV7ngYP5HmgHTQABaAhHQISbfB1s+FF1fZQoaAZHwDDhbeMyaeBoB0uSaAhHQISdwIY3vQZ1fZQoaAZHwCILM/yGzrxoB0uVaAhHQISgSw4bS7Z1fZQoaAZHwDNAiA2AG0NoB0vJaAhHQISzoh+vyLB1fZQoaAZHwDamG47Rv3toB0vBaAhHQIS0SiO/+Kl1fZQoaAZHwENE43FUADJoB0t2aAhHQIS0t7v5P/J1fZQoaAZHwDj5H5Jsfq5oB0uIaAhHQIS09fqoqCp1fZQoaAZHQDFahUR3/xVoB0vGaAhHQIS1KHdoFmp1fZQoaAZHwCrkb961LJ1oB0ugaAhHQIS2Sc3EQ5F1fZQoaAZHwFTkkmQbMotoB0v0aAhHQITCUAR02cd1fZQoaAZHQECF9Vmz0H1oB03oA2gIR0CEwwBas6q9dX2UKGgGR8Axju2Zy+6AaAdLsmgIR0CEw6khzNlidX2UKGgGR8BJx2/SH/LlaAdL8mgIR0CExfxUedTYdX2UKGgGR8BAQz7di2DyaAdLf2gIR0CEyru63AmBdX2UKGgGR8BNhk6T4cm0aAdLhWgIR0CEytc5bQkYdX2UKGgGR7/ZBi1Aqur7aAdLi2gIR0CEzISzw+dLdX2UKGgGR8AeRATqSowVaAdLlmgIR0CEzwFZgXuWdX2UKGgGR0An60svqTr3aAdLpWgIR0CE0HhScbzcdX2UKGgGR0Atn/echC+laAdLe2gIR0CE1OPy08eTdX2UKGgGR0BCPIaDPGADaAdLeWgIR0CE1TrWy1NQdX2UKGgGR8BJcFwDNhVmaAdLsGgIR0CE3FLKV6eHdX2UKGgGR0Ar6oc7yQPqaAdLmWgIR0CE3Iliz9jxdX2UKGgGR0BNYLVWjoIOaAdN6ANoCEdAhN6vQF9roHV9lChoBkfAJb4J/oaDPGgHS4poCEdAhOEcQAdXDHV9lChoBkfANmqNhmXgL2gHS3NoCEdAhOG3LV4HHHV9lChoBkdAISEqlP8AJmgHS5xoCEdAhOH1toBaLXV9lChoBkdACt10T101ZWgHS3toCEdAhOem8274BXVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 70, "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": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz8zqSowVTJhhZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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": "2.3.1+cu121", "GPU Enabled": "False", "Numpy": "1.26.4", "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:e172e932fa526f1713452762a7f1213e315d9251018ea596f45e21630a280a2c
+size 147463

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 0x7c85d2bdecb0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7c85d2bded40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7c85d2bdedd0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7c85d2bdee60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7c85d2bdeef0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7c85d2bdef80>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7c85d2bdf010>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7c85d2bdf0a0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7c85d2bdf130>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7c85d2bdf1c0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7c85d2bdf250>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7c85d2bdf2e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7c85d2b88200>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 229376,
+    "_total_timesteps": 200000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1723557604271794153,
+    "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": -0.1468799999999999,
+    "_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": 70,
+    "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": 2048,
+    "gamma": 0.99,
+    "gae_lambda": 0.95,
+    "ent_coef": 0.0,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 10,
+    "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:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d4b7bd134528b34c4e7d1053ac4bb8228760306cdd93b5d8e98152dcb603049
+size 87978

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e9720e2f73f4d803762c0c4ad9283909d91a25cfe65373d36a120dd406499cb6
+size 43634

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: 2.3.1+cu121
+- GPU Enabled: False
+- Numpy: 1.26.4
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": -121.23810369999998, "std_reward": 13.257275028191941, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-08-13T14:16:50.886889"}