First attempt

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: 248.73 +/- 20.21
+      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 0x7f1f26198280>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1f26198310>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1f261983a0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1f26198430>", "_build": "<function ActorCriticPolicy._build at 0x7f1f261984c0>", "forward": "<function ActorCriticPolicy.forward at 0x7f1f26198550>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1f261985e0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1f26198670>", "_predict": "<function ActorCriticPolicy._predict at 0x7f1f26198700>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1f26198790>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1f26198820>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1f261988b0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1f26193870>"}, "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": 1678099612444992231, "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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVdxAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMI+Wncm98SXkCUhpRSlIwBbJRN6AOMAXSUR0CW6hz+3pfQdX2UKGgGaAloD0MIMpBnl++vZUCUhpRSlGgVTegDaBZHQJbyaKNyYHB1fZQoaAZoCWgPQwjwT6kS5TtmQJSGlFKUaBVN6ANoFkdAlvJ5vcafjHV9lChoBmgJaA9DCNTRcTWy8WFAlIaUUpRoFU3oA2gWR0CW82VcD8tPdX2UKGgGaAloD0MIMevFUE43Y0CUhpRSlGgVTegDaBZHQJb0fQJHAh11fZQoaAZoCWgPQwgDzHwHv4RmQJSGlFKUaBVN6ANoFkdAlwLak/KQrHV9lChoBmgJaA9DCJawNsbO6WFAlIaUUpRoFU3oA2gWR0CXA1fnwG4adX2UKGgGaAloD0MIL2mM1tEbaECUhpRSlGgVTegDaBZHQJcI9Whh6Sl1fZQoaAZoCWgPQwgkJqjhW+RcQJSGlFKUaBVN6ANoFkdAlw53XiBGx3V9lChoBmgJaA9DCL3IBPwaZV9AlIaUUpRoFU3oA2gWR0CXKqm8/UvxdX2UKGgGaAloD0MILo81IwNzY0CUhpRSlGgVTegDaBZHQJct/W/ag291fZQoaAZoCWgPQwhoQL0ZtTJoQJSGlFKUaBVN6ANoFkdAly4bY9Pk73V9lChoBmgJaA9DCGNEotAyEWVAlIaUUpRoFU3oA2gWR0CXNwCPZIxydX2UKGgGaAloD0MIq7LvimCZZUCUhpRSlGgVTegDaBZHQJc4VHSWqtJ1fZQoaAZoCWgPQwhiaeBHtZxhQJSGlFKUaBVN6ANoFkdAlzmfA0sOG3V9lChoBmgJaA9DCJZ2ai43plxAlIaUUpRoFU3oA2gWR0CXPHKdQO4HdX2UKGgGaAloD0MIu2JGePvvYUCUhpRSlGgVTegDaBZHQJdBXabnX/Z1fZQoaAZoCWgPQwhK0F/okaNjQJSGlFKUaBVN6ANoFkdAl0dNYSxqwnV9lChoBmgJaA9DCFDEIoYdImNAlIaUUpRoFU3oA2gWR0CXR1zfrKNidX2UKGgGaAloD0MIiXssfWiVYUCUhpRSlGgVTegDaBZHQJdIM8V58jR1fZQoaAZoCWgPQwi6aMh4lM9lQJSGlFKUaBVN6ANoFkdAl0k1JcxCY3V9lChoBmgJaA9DCPyKNVzkhFJAlIaUUpRoFUvbaBZHQJdTTBXS0Bx1fZQoaAZoCWgPQwgAH7x2aQpmQJSGlFKUaBVN6ANoFkdAl1eDJ+2E03V9lChoBmgJaA9DCIWy8PU1/mBAlIaUUpRoFU3oA2gWR0CXWB0uUUwjdX2UKGgGaAloD0MIA0GADB0PZkCUhpRSlGgVTegDaBZHQJdfhL127nR1fZQoaAZoCWgPQwjsZ7EUyQFEQJSGlFKUaBVLumgWR0CXY0yxzJZGdX2UKGgGaAloD0MI740hALgoZECUhpRSlGgVTegDaBZHQJdmbs7dSEV1fZQoaAZoCWgPQwhlcmpnmDRmQJSGlFKUaBVN6ANoFkdAl2jrADaGpXV9lChoBmgJaA9DCO/mqQ65dTFAlIaUUpRoFUvyaBZHQJdpvcafjCJ1fZQoaAZoCWgPQwjSj4ZTZi1iQJSGlFKUaBVN6ANoFkdAl38uoDPnjnV9lChoBmgJaA9DCFitTPglrmVAlIaUUpRoFU3oA2gWR0CXf0eMAFPjdX2UKGgGaAloD0MIxZEHIos1ZkCUhpRSlGgVTegDaBZHQJeHhZlnRLN1fZQoaAZoCWgPQwhTlEvjF+FkQJSGlFKUaBVN6ANoFkdAl4jMURFqjHV9lChoBmgJaA9DCBHF5A0wyFxAlIaUUpRoFU3oA2gWR0CXihV1fVqfdX2UKGgGaAloD0MIofSFkHN7ZUCUhpRSlGgVTegDaBZHQJeNGgRK6Fx1fZQoaAZoCWgPQwicGf1ouHZjQJSGlFKUaBVN6ANoFkdAl5QBUWEbpHV9lChoBmgJaA9DCOtXOh8ePWNAlIaUUpRoFU3oA2gWR0CXnaaisXBQdX2UKGgGaAloD0MIDeNuEK2fYECUhpRSlGgVTegDaBZHQJefKCBf8dh1fZQoaAZoCWgPQwjNlNbfEvBgQJSGlFKUaBVN6ANoFkdAl6Cp1aGHpXV9lChoBmgJaA9DCO8a9KU36mNAlIaUUpRoFU3oA2gWR0CXrmRKpT/AdX2UKGgGaAloD0MISMK+ncRgYECUhpRSlGgVTegDaBZHQJe0FkZrHlx1fZQoaAZoCWgPQwgqApzeRftmQJSGlFKUaBVN6ANoFkdAl7bQ6dUbUHV9lChoBmgJaA9DCLG/7J48wmJAlIaUUpRoFU3oA2gWR0CXuVBtUGVzdX2UKGgGaAloD0MIpFLsaJyNY0CUhpRSlGgVTegDaBZHQJe7+RQrMC91fZQoaAZoCWgPQwjsM2d9ymJkQJSGlFKUaBVN6ANoFkdAl7zhXr+o+HV9lChoBmgJaA9DCH6MuWuJXGVAlIaUUpRoFU3oA2gWR0CX1nwhW5pbdX2UKGgGaAloD0MIJVzII7hnX0CUhpRSlGgVTegDaBZHQJfWpWCEpRZ1fZQoaAZoCWgPQwhlNPJ5xUddQJSGlFKUaBVN6ANoFkdAl+BSE6DGtXV9lChoBmgJaA9DCBb4im69MWFAlIaUUpRoFU3oA2gWR0CX4bJj2BatdX2UKGgGaAloD0MIfuNrzyzVY0CUhpRSlGgVTegDaBZHQJfi/1/Ue+51fZQoaAZoCWgPQwj+0TdpmiBhQJSGlFKUaBVN6ANoFkdAl+YxqfvnbXV9lChoBmgJaA9DCHE6yVaXqWJAlIaUUpRoFU3oA2gWR0CX6xiUgSvldX2UKGgGaAloD0MILEme6/ufY0CUhpRSlGgVTegDaBZHQJfxNhYvFm51fZQoaAZoCWgPQwhf61IjdIhgQJSGlFKUaBVN6ANoFkdAl/I2ce8wpXV9lChoBmgJaA9DCCxHyECeEmFAlIaUUpRoFU3oA2gWR0CX81UfgaWHdX2UKGgGaAloD0MIQiWuY1w1TkCUhpRSlGgVS79oFkdAl/waXOW0JHV9lChoBmgJaA9DCPfmN0w0gEdAlIaUUpRoFUvLaBZHQJf/NuGbkOt1fZQoaAZoCWgPQwgiVKnZAzlgQJSGlFKUaBVN6ANoFkdAmAFulsP8RHV9lChoBmgJaA9DCPMeZ5qwz2VAlIaUUpRoFU3oA2gWR0CYCMH9m6GydX2UKGgGaAloD0MI04TtJ+ORY0CUhpRSlGgVTegDaBZHQJgMgV/MGHJ1fZQoaAZoCWgPQwibx2EwfwleQJSGlFKUaBVN6ANoFkdAmBAzW07bL3V9lChoBmgJaA9DCOGZ0CSxIl1AlIaUUpRoFU3oA2gWR0CYFEpr1uiwdX2UKGgGaAloD0MI5ueGpmwlYECUhpRSlGgVTegDaBZHQJgVdWilBQh1fZQoaAZoCWgPQwgVUn5S7QZlQJSGlFKUaBVN6ANoFkdAmBefmYBvJnV9lChoBmgJaA9DCGRZMPHH/2FAlIaUUpRoFU3oA2gWR0CYF71DjR2KdX2UKGgGaAloD0MIzAcEOpN0RECUhpRSlGgVS75oFkdAmDKnwkPcz3V9lChoBmgJaA9DCJQRF4BGOWdAlIaUUpRoFU3oA2gWR0CYM+wcHWz4dX2UKGgGaAloD0MIcQSpFDudY0CUhpRSlGgVTegDaBZHQJg1NNGmUGF1fZQoaAZoCWgPQwhDHVa4ZepmQJSGlFKUaBVN6ANoFkdAmDaGLtNSInV9lChoBmgJaA9DCCsWvymsGDFAlIaUUpRoFUvfaBZHQJg3oFfReC11fZQoaAZoCWgPQwiW6CyziJNhQJSGlFKUaBVN6ANoFkdAmDmRvR7Z4HV9lChoBmgJaA9DCH433bJDzEZAlIaUUpRoFUuyaBZHQJg+GdDpkf91fZQoaAZoCWgPQwg0hGOWvSdkQJSGlFKUaBVN6ANoFkdAmD7u9Ba9snV9lChoBmgJaA9DCDPfwU8c9DVAlIaUUpRoFUvnaBZHQJhHbgk1Muh1fZQoaAZoCWgPQwiaXfdWJJ9hQJSGlFKUaBVN6ANoFkdAmEicFyJbdXV9lChoBmgJaA9DCLsp5bUS82FAlIaUUpRoFU3oA2gWR0CYVUH/tICmdX2UKGgGaAloD0MIU1p/S4BRZUCUhpRSlGgVTegDaBZHQJhYZNahYeV1fZQoaAZoCWgPQwg486s5QNdhQJSGlFKUaBVN6ANoFkdAmFqahcqvvHV9lChoBmgJaA9DCKpm1lJAyj1AlIaUUpRoFUufaBZHQJhfCuaF23d1fZQoaAZoCWgPQwgCEHf1Kn9iQJSGlFKUaBVN6ANoFkdAmF/Rje9BbHV9lChoBmgJaA9DCPUtc7osMmBAlIaUUpRoFU3oA2gWR0CYYj2LpA2RdX2UKGgGaAloD0MIkNjuHqArZUCUhpRSlGgVTegDaBZHQJhkgO2AoXt1fZQoaAZoCWgPQwjqy9JOTQBhQJSGlFKUaBVN6ANoFkdAmGff5tWMj3V9lChoBmgJaA9DCAZM4NbdX19AlIaUUpRoFU3oA2gWR0CYaboouwotdX2UKGgGaAloD0MI1lOrry4LZUCUhpRSlGgVTegDaBZHQJiMPJW/8EV1fZQoaAZoCWgPQwjVyoRfai9lQJSGlFKUaBVN6ANoFkdAmI8ayrxRVXV9lChoBmgJaA9DCP0WnSw1zWNAlIaUUpRoFU3oA2gWR0CYkIBw++uedX2UKGgGaAloD0MIlGdeDjs9ZUCUhpRSlGgVTegDaBZHQJiTzS+g13t1fZQoaAZoCWgPQwhW0/VEV+FlQJSGlFKUaBVN6ANoFkdAmJiL3XZoPHV9lChoBmgJaA9DCIEKR5DKrWNAlIaUUpRoFU3oA2gWR0CYmSte2NNrdX2UKGgGaAloD0MIfH+D9uqoYUCUhpRSlGgVTegDaBZHQJifo9bHIZJ1fZQoaAZoCWgPQwg5fT1fs89kQJSGlFKUaBVN6ANoFkdAmKB4acZtN3V9lChoBmgJaA9DCCEFTyHXK2RAlIaUUpRoFU3oA2gWR0CYr0nYQJ5WdX2UKGgGaAloD0MIyhmKO94DaUCUhpRSlGgVTegDaBZHQJix5SHdoFp1fZQoaAZoCWgPQwi6awn5oO5jQJSGlFKUaBVN6ANoFkdAmLlad1+y7nV9lChoBmgJaA9DCHRd+MH5y2FAlIaUUpRoFU3oA2gWR0CYupbFS88LdX2UKGgGaAloD0MIcTs0LMYZZUCUhpRSlGgVTegDaBZHQJi+Vg2Ifr91fZQoaAZoCWgPQwjXM4RjFhBiQJSGlFKUaBVN6ANoFkdAmMH0YGdI5HV9lChoBmgJaA9DCMMQOX09LGRAlIaUUpRoFU3oA2gWR0CYxoaM72csdX2UKGgGaAloD0MIYmafx6hIZECUhpRSlGgVTegDaBZHQJjIg5wOvuB1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 252, "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": "1.22.4", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a457c34336d99ffa728e1afbc478c2aad6a02f09b6de9aa8279aa74f1aa79f16
+size 147412

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 0x7f1f26198280>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f1f26198310>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f1f261983a0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f1f26198430>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f1f261984c0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f1f26198550>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f1f261985e0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f1f26198670>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f1f26198700>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f1f26198790>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f1f26198820>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f1f261988b0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f1f26193870>"
+    },
+    "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": 1678099612444992231,
+    "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": 252,
+    "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 sha256:5c9ff730740bdb1ba87047576b2acce6c6f284584a70df3d3cd35af07b5918f6
+size 87929

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:367c7b8e758bdf3d66dd370e7ee2252ecb7b25042cd6269c68cef880b39dd6eb
+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: 1.22.4
+- Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 248.73338719924232, "std_reward": 20.20823799724854, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-06T11:28:55.824834"}