upload model

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: 264.33 +/- 19.51
+      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 0x7fbfbc4d2550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbfbc4d25e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbfbc4d2670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbfbc4d2700>", "_build": "<function ActorCriticPolicy._build at 0x7fbfbc4d2790>", "forward": "<function ActorCriticPolicy.forward at 0x7fbfbc4d2820>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbfbc4d28b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbfbc4d2940>", "_predict": "<function ActorCriticPolicy._predict at 0x7fbfbc4d29d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbfbc4d2a60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbfbc4d2af0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbfbc4d2b80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fbfbc4d7180>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678717519213986585, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOS9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_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, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVcxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIE7u2t9uBcECUhpRSlIwBbJRNKQGMAXSUR0CSE1AskIHDdX2UKGgGaAloD0MI4Qz+fjGjbUCUhpRSlGgVTQ0BaBZHQJIUCMir1dx1fZQoaAZoCWgPQwjPaRZod2xyQJSGlFKUaBVNTAFoFkdAkhRxDgIhQnV9lChoBmgJaA9DCGAfnbpyZW5AlIaUUpRoFU0aAWgWR0CSFK6+36RAdX2UKGgGaAloD0MIe5+qQoNNcUCUhpRSlGgVTTYBaBZHQJIWD3Gn4wh1fZQoaAZoCWgPQwgfLGND91NyQJSGlFKUaBVNLQFoFkdAkhhftlZownV9lChoBmgJaA9DCA034PODH3BAlIaUUpRoFU04AWgWR0CSGVPQOWjXdX2UKGgGaAloD0MIcuFASJbYcECUhpRSlGgVTT0BaBZHQJIaJQGfPHF1fZQoaAZoCWgPQwjlCu9yEYxvQJSGlFKUaBVNCQFoFkdAkhsV1bJOnHV9lChoBmgJaA9DCCpz843oYm1AlIaUUpRoFU17AWgWR0CSG1Hz6JqJdX2UKGgGaAloD0MIycovg7GcbECUhpRSlGgVTSIBaBZHQJIcHk8zQ/p1fZQoaAZoCWgPQwjTMlLvqchvQJSGlFKUaBVNHQFoFkdAkhxAWepXIXV9lChoBmgJaA9DCHAJwD/lpHBAlIaUUpRoFU0hAWgWR0CSHXrz5GjLdX2UKGgGaAloD0MITPp7KXwxcUCUhpRSlGgVTWIBaBZHQJIeLOpsGgV1fZQoaAZoCWgPQwigU5CfjaZyQJSGlFKUaBVNfAFoFkdAkh6Dp1RtQHV9lChoBmgJaA9DCCekNQad8G9AlIaUUpRoFU0IAWgWR0CSHrlhgE2YdX2UKGgGaAloD0MIOPdXj/vybkCUhpRSlGgVTQoBaBZHQJIfFUfgaWJ1fZQoaAZoCWgPQwiEuHL2jnhxQJSGlFKUaBVNJQFoFkdAkiEQvcrRSnV9lChoBmgJaA9DCEKY271cv21AlIaUUpRoFU0EAWgWR0CSISSLIgeSdX2UKGgGaAloD0MIfc7drteKckCUhpRSlGgVTRoBaBZHQJIjT3+MqBp1fZQoaAZoCWgPQwgEcokjj4BxQJSGlFKUaBVNCQFoFkdAkiNXL3bmEHV9lChoBmgJaA9DCP/pBgp8V3BAlIaUUpRoFU2HAWgWR0CSI5HlOoHcdX2UKGgGaAloD0MIGhcOhGShcECUhpRSlGgVTYEBaBZHQJIjsf2bobJ1fZQoaAZoCWgPQwiinGhXIclEQJSGlFKUaBVLzWgWR0CSJNXLeQ+2dX2UKGgGaAloD0MIigCnd/FtcECUhpRSlGgVTSEBaBZHQJIlIpuuRtB1fZQoaAZoCWgPQwgYCW05lwdxQJSGlFKUaBVNQAFoFkdAkia7fpD/l3V9lChoBmgJaA9DCH8TChHwsW5AlIaUUpRoFU0hAWgWR0CSJsg2qDK6dX2UKGgGaAloD0MIW0QUk7eUb0CUhpRSlGgVTQIBaBZHQJInCOOsDGN1fZQoaAZoCWgPQwg429yYnkdxQJSGlFKUaBVNUAFoFkdAkidSvovBanV9lChoBmgJaA9DCKhSswdan3BAlIaUUpRoFU1JAWgWR0CSKG4jbBXTdX2UKGgGaAloD0MIlN43vrYwckCUhpRSlGgVTUYBaBZHQJIow+C9RJp1fZQoaAZoCWgPQwi/ub96nN5yQJSGlFKUaBVNnQFoFkdAkijZc5bQkXV9lChoBmgJaA9DCPrPmh9/5m9AlIaUUpRoFU0hAWgWR0CSKbaNuLrHdX2UKGgGaAloD0MI9WT+0fctckCUhpRSlGgVTRYBaBZHQJIrjg0j1PF1fZQoaAZoCWgPQwjxD1t6NNtyQJSGlFKUaBVNHAFoFkdAkiv9z4k/r3V9lChoBmgJaA9DCKhSswfa129AlIaUUpRoFU0nAWgWR0CSLHwZflZHdX2UKGgGaAloD0MIhq+vdWnPcUCUhpRSlGgVTRIBaBZHQJItiGzru6V1fZQoaAZoCWgPQwgK16NwPUJuQJSGlFKUaBVNVQJoFkdAki3qshgVoHV9lChoBmgJaA9DCO9v0F69ZXNAlIaUUpRoFU0rAWgWR0CSLhEFnqVydX2UKGgGaAloD0MILxfxnZiicECUhpRSlGgVTXIBaBZHQJIumVB2Ohl1fZQoaAZoCWgPQwhB8zl3O0hvQJSGlFKUaBVNAAFoFkdAki6jBuXNT3V9lChoBmgJaA9DCJQUWADTam9AlIaUUpRoFU04AWgWR0CSRAIPbwjMdX2UKGgGaAloD0MI5+RFJiAgcUCUhpRSlGgVTVsBaBZHQJJEkLux8lZ1fZQoaAZoCWgPQwiS6GUUSzZzQJSGlFKUaBVNLwFoFkdAkkT2PxQSBnV9lChoBmgJaA9DCCuGqwNg23JAlIaUUpRoFU0CAWgWR0CSRPagElmfdX2UKGgGaAloD0MIrP9zmC88cECUhpRSlGgVTTQBaBZHQJJFcPrfLs91fZQoaAZoCWgPQwiynlp99chsQJSGlFKUaBVNPgFoFkdAkkXK+nIhhnV9lChoBmgJaA9DCE2CN6TRmXBAlIaUUpRoFU0GAWgWR0CSRuo/iYLLdX2UKGgGaAloD0MI9SoyOiBEbECUhpRSlGgVTa8BaBZHQJJHTnnuAqd1fZQoaAZoCWgPQwju68A5IwJGQJSGlFKUaBVL6mgWR0CSR87j1f3OdX2UKGgGaAloD0MIotEdxM6TcECUhpRSlGgVTSEBaBZHQJJIBamoBJZ1fZQoaAZoCWgPQwjvVpboLI1vQJSGlFKUaBVNLAFoFkdAkkj13hXKbXV9lChoBmgJaA9DCNF4IojzK3BAlIaUUpRoFUvwaBZHQJJJDuhK15V1fZQoaAZoCWgPQwgnhA66xEhzQJSGlFKUaBVNBgFoFkdAkklAU1yeZ3V9lChoBmgJaA9DCP9YiA5B/nBAlIaUUpRoFUv8aBZHQJJJgHlfZ291fZQoaAZoCWgPQwjEeM2renFxQJSGlFKUaBVNFAFoFkdAkkmTfrKNhnV9lChoBmgJaA9DCDSEY5a9p29AlIaUUpRoFUvvaBZHQJJL4RmK64F1fZQoaAZoCWgPQwjk9PV8zeBCQJSGlFKUaBVL8GgWR0CSTgOn2qT9dX2UKGgGaAloD0MI/7Pmx19MckCUhpRSlGgVTRwBaBZHQJJOHyauwHJ1fZQoaAZoCWgPQwhbzqW46kZyQJSGlFKUaBVNHQFoFkdAkk9PLgXMyXV9lChoBmgJaA9DCI47pYN1ZXBAlIaUUpRoFU1ZAWgWR0CSUVUKArhBdX2UKGgGaAloD0MIGeWZl8PfcUCUhpRSlGgVS/poFkdAklGNcGC7LHV9lChoBmgJaA9DCDsA4q5eRUhAlIaUUpRoFUvbaBZHQJJRtA4XGfh1fZQoaAZoCWgPQwikq3R3XTlxQJSGlFKUaBVNLQFoFkdAklJsZgogFHV9lChoBmgJaA9DCFoO9FCbBnFAlIaUUpRoFU0BAWgWR0CSU5kbgjyGdX2UKGgGaAloD0MIMgOV8e8ScECUhpRSlGgVTQoBaBZHQJJUTGEPDpF1fZQoaAZoCWgPQwicGf1o+EhwQJSGlFKUaBVNSgFoFkdAklRoOQQtjHV9lChoBmgJaA9DCBPwayRJx3FAlIaUUpRoFU0MAWgWR0CSVLX7+DODdX2UKGgGaAloD0MIAYV6+ggbcECUhpRSlGgVTT8BaBZHQJJVFC7btZ51fZQoaAZoCWgPQwhJvady2v5iQJSGlFKUaBVN6ANoFkdAklY7CJoCdXV9lChoBmgJaA9DCLH34os2r3NAlIaUUpRoFU04AWgWR0CSVpB9Tgl4dX2UKGgGaAloD0MI2v6VlSZXZkCUhpRSlGgVTdYBaBZHQJJW1FgDzRR1fZQoaAZoCWgPQwiZ8Ev9fIhxQJSGlFKUaBVNGAFoFkdAkleUgntv43V9lChoBmgJaA9DCOp5NxaU0HBAlIaUUpRoFU0HAWgWR0CSWSBkZrHmdX2UKGgGaAloD0MIjX3JxoMtcECUhpRSlGgVTQYBaBZHQJJZMq7ROUN1fZQoaAZoCWgPQwjzBS0koKtwQJSGlFKUaBVL+2gWR0CSWd7qptJndX2UKGgGaAloD0MIBVH3AUhtbkCUhpRSlGgVS/JoFkdAkltI51eSjnV9lChoBmgJaA9DCOif4GLFMHFAlIaUUpRoFU0oAWgWR0CSXJspG4I9dX2UKGgGaAloD0MIh07Pu/EwcECUhpRSlGgVTRoBaBZHQJJc4bYK6Wh1fZQoaAZoCWgPQwiVYdwN4htyQJSGlFKUaBVNLgFoFkdAklzqCtihFnV9lChoBmgJaA9DCAYsuYrFr3FAlIaUUpRoFU0GAWgWR0CSXa4ptrKvdX2UKGgGaAloD0MIqtIW1/hzcUCUhpRSlGgVTQ8BaBZHQJJdq+wkgOl1fZQoaAZoCWgPQwjq6LgaWYRuQJSGlFKUaBVNHgFoFkdAkl4fVmSQo3V9lChoBmgJaA9DCD9XW7E/Vm5AlIaUUpRoFU0cAWgWR0CSXnm6XjU/dX2UKGgGaAloD0MIwF5hwX3ockCUhpRSlGgVTRUBaBZHQJJfDatcOb11fZQoaAZoCWgPQwjkDwaee75vQJSGlFKUaBVNUwFoFkdAkl8Mt9QXRHV9lChoBmgJaA9DCJ1Jm6p7vlFAlIaUUpRoFUvOaBZHQJJfNWn0kGB1fZQoaAZoCWgPQwh6GFqdHENuQJSGlFKUaBVNDwFoFkdAkl9DJMg2ZXV9lChoBmgJaA9DCKPp7GRwOW5AlIaUUpRoFU0bAWgWR0CSX2aCtihGdX2UKGgGaAloD0MIXoJTH0g8TECUhpRSlGgVS9loFkdAkl92CyyD7XV9lChoBmgJaA9DCGeAC7IlxHBAlIaUUpRoFU0SAWgWR0CSX7TLGJemdX2UKGgGaAloD0MIK2nFNxRdcUCUhpRSlGgVTQcBaBZHQJJgnBrN4aB1fZQoaAZoCWgPQwgaa39n+yxvQJSGlFKUaBVL+GgWR0CSYrO/+Kj0dX2UKGgGaAloD0MIRDaQLnaMcECUhpRSlGgVTUgBaBZHQJJjeZ/kNnZ1fZQoaAZoCWgPQwiD+pY53UdzQJSGlFKUaBVNIQFoFkdAkmO0bgjyF3V9lChoBmgJaA9DCHo1QGnornBAlIaUUpRoFU0dAWgWR0CSY91NxlxwdX2UKGgGaAloD0MIwk6xatBUcECUhpRSlGgVTREBaBZHQJJkXqt5le51fZQoaAZoCWgPQwjVP4hkCHFwQJSGlFKUaBVNBgFoFkdAkmSLsByS3nV9lChoBmgJaA9DCO8AT1p4KXBAlIaUUpRoFU0eAWgWR0CSZLwVCXyBdX2UKGgGaAloD0MI1m8mpgu/TECUhpRSlGgVS/NoFkdAkmVb8BMi8nVlLg=="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:cc2afee5ebcec422eac9eaa71f6f4e8c1fd5457f62333464e3c13bc687a93f80
+size 147413

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+1.7.0

ppo-LunarLander-v2/data

@@ -0,0 +1,95 @@
+{
+    "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 0x7fbfbc4d2550>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fbfbc4d25e0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fbfbc4d2670>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fbfbc4d2700>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fbfbc4d2790>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fbfbc4d2820>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fbfbc4d28b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fbfbc4d2940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fbfbc4d29d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fbfbc4d2a60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fbfbc4d2af0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fbfbc4d2b80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7fbfbc4d7180>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "observation_space": {
+        ":type:": "<class 'gym.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "_shape": [
+            8
+        ],
+        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
+        "high": "[inf inf inf inf inf inf inf inf]",
+        "bounded_below": "[False False False False False False False False]",
+        "bounded_above": "[False False False False False False False False]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1678717519213986585,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_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,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 248,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8876f07ba5a6826a89c716a4ab555679ec3571e121f5a5db977d08cd313b90f3
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0ad558fffa610c5d3d7547132f83f70a3a6fb9d32976c1e4e94d9334f20e772b
+size 43393

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 +1,7 @@
+- OS: Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.9.16
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 264.32755417273734, "std_reward": 19.50515630735238, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-13T14:46:21.722739"}