test1

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: -137.23 +/- 89.42
+      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 0x7f3374e1e040>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3374e1e0d0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3374e1e160>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3374e1e1f0>", "_build": "<function ActorCriticPolicy._build at 0x7f3374e1e280>", "forward": "<function ActorCriticPolicy.forward at 0x7f3374e1e310>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3374e1e3a0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3374e1e430>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3374e1e4c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3374e1e550>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3374e1e5e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3374e1e670>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f3374e195d0>"}, "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": 114688, "_total_timesteps": 100000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1676623524573067081, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+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": -0.1468799999999999, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI6iEa3UF+TsCUhpRSlIwBbJRLYowBdJRHQGAfgCfYjB51fZQoaAZoCWgPQwj3ksZoHdlQwJSGlFKUaBVLR2gWR0BgIF5jYqXodX2UKGgGaAloD0MIrVEP0ehhUcCUhpRSlGgVS0loFkdAYCBdZ7ojfXV9lChoBmgJaA9DCHU+PEuQwFTAlIaUUpRoFUtWaBZHQGAgXB55Z8t1fZQoaAZoCWgPQwh8Zd6qaxtgwJSGlFKUaBVLbGgWR0BgIWdf9gnddX2UKGgGaAloD0MIXeFdLuIYXcCUhpRSlGgVS5VoFkdAYCTovBacJHV9lChoBmgJaA9DCE2jycWYc2TAlIaUUpRoFUtqaBZHQGAlhA4XGfh1fZQoaAZoCWgPQwi6vaQxWoFJwJSGlFKUaBVLeGgWR0BgKioOx0MgdX2UKGgGaAloD0MIKNGSx9MuWMCUhpRSlGgVS1hoFkdAYCzztCzC13V9lChoBmgJaA9DCAEUI0vmekXAlIaUUpRoFUtzaBZHQGAtRB3Roh91fZQoaAZoCWgPQwhOYDqt24hawJSGlFKUaBVLX2gWR0BgLeoYNy5qdX2UKGgGaAloD0MIk//J3704YsCUhpRSlGgVS5NoFkdAYC68uBczInV9lChoBmgJaA9DCLu5+NueKDTAlIaUUpRoFUtnaBZHQGA1ZSWJJoV1fZQoaAZoCWgPQwgKndfYJUtWwJSGlFKUaBVLYmgWR0BgNyX8fmtAdX2UKGgGaAloD0MIJoxmZfuXWMCUhpRSlGgVS15oFkdAYDoDFId2gXV9lChoBmgJaA9DCOBlho2yAGbAlIaUUpRoFUt+aBZHQGA7ihN/OMV1fZQoaAZoCWgPQwgVAOMZNOhXwJSGlFKUaBVLZmgWR0BgO4HeJpFkdX2UKGgGaAloD0MIij+KOnOzZsCUhpRSlGgVS3BoFkdAYD6qCpWFOHV9lChoBmgJaA9DCJ4LI72o7GXAlIaUUpRoFUtyaBZHQGA/KoqCpWF1fZQoaAZoCWgPQwg/Gk6ZmxhRwJSGlFKUaBVLRWgWR0BgQJZQpF1CdX2UKGgGaAloD0MI/WmjOh2QTcCUhpRSlGgVS3poFkdAYECJmdy1eHV9lChoBmgJaA9DCNV1qKYkPlnAlIaUUpRoFUtuaBZHQGBCgbQ1JlJ1fZQoaAZoCWgPQwjHLHsSWBVmwJSGlFKUaBVLX2gWR0BgRLFZPl+3dX2UKGgGaAloD0MIYkok0cu8RMCUhpRSlGgVS3JoFkdAYESotthuwXV9lChoBmgJaA9DCMbf9gSJ+VHAlIaUUpRoFUtiaBZHQGBIe1Bt1p11fZQoaAZoCWgPQwjejQWFQWNPwJSGlFKUaBVLSmgWR0BgSp3Roh6jdX2UKGgGaAloD0MIU7RyL7D3aMCUhpRSlGgVS2loFkdAYEveSB9TgnV9lChoBmgJaA9DCM+9h0uOOVXAlIaUUpRoFUtzaBZHQGBPeERJ2+x1fZQoaAZoCWgPQwjpfeNrz+dkwJSGlFKUaBVLYmgWR0BgVq3kPtladX2UKGgGaAloD0MIYKsEi8OBQMCUhpRSlGgVS3FoFkdAYFfo+wC8vnV9lChoBmgJaA9DCBiV1AloSlzAlIaUUpRoFUtjaBZHQGBYkAPuogp1fZQoaAZoCWgPQwiDaRg+IoVSwJSGlFKUaBVLRmgWR0BgWM3AEdNndX2UKGgGaAloD0MIggLv5NO6U8CUhpRSlGgVS1poFkdAYFrrnDBMz3V9lChoBmgJaA9DCFsnLscrWkTAlIaUUpRoFUtlaBZHQGBd+MIeHSF1fZQoaAZoCWgPQwjdXPxtT6hRwJSGlFKUaBVLb2gWR0BgX7pu/DcedX2UKGgGaAloD0MIAoOkT6uWVsCUhpRSlGgVS1NoFkdAYGFlA/s3Q3V9lChoBmgJaA9DCJynOuRmU1PAlIaUUpRoFUtiaBZHQGBht3GGVRl1fZQoaAZoCWgPQwi2EU92M4lRwJSGlFKUaBVLa2gWR0BgYj81n/T9dX2UKGgGaAloD0MIz4QmiSVhTsCUhpRSlGgVS1BoFkdAYGNiVB2OhnV9lChoBmgJaA9DCO2fpwGD2kbAlIaUUpRoFUuLaBZHQGBnKkl/pdN1fZQoaAZoCWgPQwiun/6z5j1JwJSGlFKUaBVLnmgWR0Bgaf9WIXTFdX2UKGgGaAloD0MI86/llesxUsCUhpRSlGgVS3BoFkdAYHB/rB0p3HV9lChoBmgJaA9DCKBtNeuMiz7AlIaUUpRoFUtYaBZHQGBw0f5k9U11fZQoaAZoCWgPQwjlCYSdYqVbwJSGlFKUaBVLUmgWR0BgcVANXo1UdX2UKGgGaAloD0MI6xotB/pzaMCUhpRSlGgVS4NoFkdAYHGpXp4bCXV9lChoBmgJaA9DCE6dR8X/8VXAlIaUUpRoFUtKaBZHQGBz/HPu5SZ1fZQoaAZoCWgPQwiuZTIcz8dRwJSGlFKUaBVLTWgWR0BgdjzAeq7zdX2UKGgGaAloD0MIMsfyrnomUsCUhpRSlGgVS0doFkdAYHcr1dxAB3V9lChoBmgJaA9DCNXo1QClf1bAlIaUUpRoFUtsaBZHQGB4HIhhYvF1fZQoaAZoCWgPQwgyBADHnsRVwJSGlFKUaBVLaWgWR0Bgebz5GjKxdX2UKGgGaAloD0MIKH0h5LyyUcCUhpRSlGgVS0JoFkdAYHrWnTAnD3V9lChoBmgJaA9DCN8Xl6q0dUfAlIaUUpRoFUtgaBZHQGB9M1baAWl1fZQoaAZoCWgPQwjLvcCsUGZPwJSGlFKUaBVLhmgWR0Bgf/aHsTnJdX2UKGgGaAloD0MIINWw3xN4WcCUhpRSlGgVS1JoFkdAYIHbN8ma6XV9lChoBmgJaA9DCJzCSgUVe1DAlIaUUpRoFUt6aBZHQGCEOq//Nqx1fZQoaAZoCWgPQwh2xvfFpTRWwJSGlFKUaBVLVmgWR0BgiP9ehPCVdX2UKGgGaAloD0MIwoU8ghunTcCUhpRSlGgVS4ZoFkdAYIo5uIhyKnV9lChoBmgJaA9DCJn091J4ylPAlIaUUpRoFUtbaBZHQGCL1XeWOZN1fZQoaAZoCWgPQwg+PEuQEbdYwJSGlFKUaBVLX2gWR0BgjDZBcAzYdX2UKGgGaAloD0MIHEMAcOzBWMCUhpRSlGgVS0doFkdAYIxa6jFhonV9lChoBmgJaA9DCLDllettMFDAlIaUUpRoFUtNaBZHQGCMwUg0TDh1fZQoaAZoCWgPQwic/YFy27ZBQJSGlFKUaBVN6ANoFkdAYJNLKV6eG3V9lChoBmgJaA9DCI1GPq94LFHAlIaUUpRoFUteaBZHQGCUk8JUo8Z1fZQoaAZoCWgPQwj7V1aalKJfwJSGlFKUaBVLiGgWR0Bglz6UJOWTdX2UKGgGaAloD0MIev60UZ3wUMCUhpRSlGgVS3ZoFkdAYJeRujynUHV9lChoBmgJaA9DCOyjU1c+xU7AlIaUUpRoFUtsaBZHQGCYNUGVzIV1fZQoaAZoCWgPQwg3xk54CVRUwJSGlFKUaBVLW2gWR0BgmH69CeEqdX2UKGgGaAloD0MI4xk09E9NU8CUhpRSlGgVS01oFkdAYJjH7xd6cHV9lChoBmgJaA9DCMzR4/c2TVfAlIaUUpRoFUtHaBZHQGCasgdOqNp1fZQoaAZoCWgPQwhUVWgglnk6wJSGlFKUaBVLlGgWR0Bgmy44Ia99dX2UKGgGaAloD0MIHxFTIonARcCUhpRSlGgVS3poFkdAYJ8QcxTKknV9lChoBmgJaA9DCGeZRSi2imHAlIaUUpRoFUtgaBZHQGChaciGFi91fZQoaAZoCWgPQwiI8ZpXdURXwJSGlFKUaBVLZ2gWR0Bgoqtq59VndX2UKGgGaAloD0MINiOD3EUoVcCUhpRSlGgVSz1oFkdAYKLw2ETQFHV9lChoBmgJaA9DCPq0iv7Qkl3AlIaUUpRoFUujaBZHQGCkqASWZ7Z1fZQoaAZoCWgPQwhnZfuQtzlowJSGlFKUaBVLc2gWR0BgpQODrZ8KdX2UKGgGaAloD0MIZHWr5yQ6YcCUhpRSlGgVS0toFkdAYKWUGFBY3nV9lChoBmgJaA9DCPW+8bVnfj3AlIaUUpRoFUt1aBZHQGClskhRqGl1fZQoaAZoCWgPQwjrNT0oKFBYwJSGlFKUaBVLfmgWR0BgpfkT6BRRdX2UKGgGaAloD0MIlnhA2ZTrSsCUhpRSlGgVS05oFkdAYKcmFajesXV9lChoBmgJaA9DCDQTDOcaf1fAlIaUUpRoFUt4aBZHQGCp5XEIgNh1fZQoaAZoCWgPQwj2DUxulPlowJSGlFKUaBVLZGgWR0Bgq5vgm7aqdX2UKGgGaAloD0MIhCwLJv4YNsCUhpRSlGgVS4BoFkdAYKzQN0/4ZnV9lChoBmgJaA9DCJIHIos0AUzAlIaUUpRoFUtpaBZHQGCvEyckMTh1fZQoaAZoCWgPQwinP/uRIiBXwJSGlFKUaBVLbGgWR0BgrxPsRg7YdX2UKGgGaAloD0MIDcLc7uX+PsCUhpRSlGgVS3loFkdAYK8JeE7GN3V9lChoBmgJaA9DCMA9z582JkzAlIaUUpRoFUtmaBZHQGCyzU7Sy+p1fZQoaAZoCWgPQwg8TPvm/lI5wJSGlFKUaBVLWGgWR0Bgs01Gb1AadX2UKGgGaAloD0MIscOY9PdoW8CUhpRSlGgVS1doFkdAYLTXPJJXhnV9lChoBmgJaA9DCAQcQpWaHVTAlIaUUpRoFUtuaBZHQGC2HH/95yF1fZQoaAZoCWgPQwgyrrg4KjFRwJSGlFKUaBVLY2gWR0BgtwhIOH32dX2UKGgGaAloD0MIMv/omzT1UcCUhpRSlGgVS3VoFkdAYLlB1s+FDnV9lChoBmgJaA9DCGHGFKxx4FzAlIaUUpRoFUtdaBZHQGC7imVJL/V1fZQoaAZoCWgPQwhzZrtCX6xwwJSGlFKUaBVLdGgWR0Bgu4EW69TQdX2UKGgGaAloD0MIJm2q7pF0XcCUhpRSlGgVS1poFkdAYLyyfthNNHV9lChoBmgJaA9DCLOZQ1IL0FHAlIaUUpRoFUt6aBZHQGC8pgLJCBx1fZQoaAZoCWgPQwgVysLX14BMwJSGlFKUaBVLd2gWR0BgvW7voePrdX2UKGgGaAloD0MIIt+l1CVMU8CUhpRSlGgVS4JoFkdAYL3BTn7pFHV9lChoBmgJaA9DCME5I0p7+VnAlIaUUpRoFUtVaBZHQGC+0pEx7At1fZQoaAZoCWgPQwg6CDpa1YJOwJSGlFKUaBVLYWgWR0BgwSbhFVkudWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 28, "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:545299b3c3593e4eb1f68eee5faa83c9f0c9c56825c0346a74545c64bc804f83
+size 147287

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 0x7f3374e1e040>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3374e1e0d0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3374e1e160>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3374e1e1f0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3374e1e280>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3374e1e310>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f3374e1e3a0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3374e1e430>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3374e1e4c0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3374e1e550>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3374e1e5e0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3374e1e670>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f3374e195d0>"
+    },
+    "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": 114688,
+    "_total_timesteps": 100000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1676623524573067081,
+    "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": -0.1468799999999999,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 28,
+    "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 sha256:5a8bd85e6c9bd9c1e21af3acf47234fc8e169a3909bbf6f12db0791a6ae01279
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:985d1a131cda3937c27cad2e3d083e3d43264e29bf80b8ba5b6576a957ada378
+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-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
+- Python: 3.8.10
+- Stable-Baselines3: 1.7.0
+- PyTorch: 1.13.1+cu116
+- GPU Enabled: True
+- Numpy: 1.21.6
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": -137.22542339677457, "std_reward": 89.41815584236168, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-17T08:51:14.343743"}