Upload PPO LunarLander-v2 trained agent

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 261.80 +/- 70.13
+      value: 286.30 +/- 18.02
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f7dbfc9f0e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7dbfc9f170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7dbfc9f200>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7dbfc9f290>", "_build": "<function ActorCriticPolicy._build at 0x7f7dbfc9f320>", "forward": "<function ActorCriticPolicy.forward at 0x7f7dbfc9f3b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7dbfc9f440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7dbfc9f4d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f7dbfc9f560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7dbfc9f5f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7dbfc9f680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7dbfc9f710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f7dbfceaab0>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 64, "num_timesteps": 200015872, "_total_timesteps": 200000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678409737008144743, "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:": "gASVyAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBS0CFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -7.935999999997279e-05, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVHhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIL8A+OvXBb0CUhpRSlIwBbJRLpowBdJRHQPv7bQ7JW/91fZQoaAZoCWgPQwjzWZ4Ht9lwQJSGlFKUaBVLomgWR0D7+21LB9CvdX2UKGgGaAloD0MIgEi/fV3pcECUhpRSlGgVS59oFkdA+/ttTKkl/3V9lChoBmgJaA9DCH4BvXAnvXJAlIaUUpRoFUu+aBZHQPv7boa4tpV1fZQoaAZoCWgPQwgpzeZx2LlyQJSGlFKUaBVLtmgWR0D7+28PNmlJdX2UKGgGaAloD0MI0sQ7wBPsckCUhpRSlGgVS7xoFkdA+/tvSHEdenV9lChoBmgJaA9DCDNqvkq+33FAlIaUUpRoFUuxaBZHQPv7b5kOI691fZQoaAZoCWgPQwgS+wRQzLFxQJSGlFKUaBVLqGgWR0D7+2/83uNQdX2UKGgGaAloD0MI4PQu3g+DcECUhpRSlGgVS+xoFkdA+/twytA9m3V9lChoBmgJaA9DCGNfsvHgMHNAlIaUUpRoFUu2aBZHQPv7cMQkHD91fZQoaAZoCWgPQwj9hokGKfhxQJSGlFKUaBVLvmgWR0D7+3ELcsUZdX2UKGgGaAloD0MIob/QI0bhcUCUhpRSlGgVS5ZoFkdA+/txRFI/aHV9lChoBmgJaA9DCFFrmndcBnJAlIaUUpRoFUugaBZHQPv7cXPRiPR1fZQoaAZoCWgPQwgDste7/w9zQJSGlFKUaBVLr2gWR0D7+3U4d6sydX2UKGgGaAloD0MIUUzeADPPcUCUhpRSlGgVS69oFkdA+/t1jpC8e3V9lChoBmgJaA9DCK+w4H7A73NAlIaUUpRoFUuxaBZHQPv7doNayKN1fZQoaAZoCWgPQwh3TN2VHVlxQJSGlFKUaBVLtWgWR0D7+3aD8tPIdX2UKGgGaAloD0MITDYebDFLb0CUhpRSlGgVS7VoFkdA+/t2fDHfdnV9lChoBmgJaA9DCI3xYfayj3JAlIaUUpRoFUuXaBZHQPv7dwE/0NB1fZQoaAZoCWgPQwj0UrExL51wQJSGlFKUaBVLpmgWR0D7+3fLA57xdX2UKGgGaAloD0MIG9e/6/MHcECUhpRSlGgVS6xoFkdA+/t3yNfgJnV9lChoBmgJaA9DCKuVCb/UqnFAlIaUUpRoFUujaBZHQPv7d8WO6up1fZQoaAZoCWgPQwhEh8CRgDBxQJSGlFKUaBVLq2gWR0D7+3gCaqjrdX2UKGgGaAloD0MIQX3LnK6dc0CUhpRSlGgVS8VoFkdA+/t4hAOav3V9lChoBmgJaA9DCIejq3Q36nBAlIaUUpRoFUugaBZHQPv7eUXO4Xp1fZQoaAZoCWgPQwjE0VW6e/9yQJSGlFKUaBVLvGgWR0D7+3l0Syt3dX2UKGgGaAloD0MIlYCYhEtCcUCUhpRSlGgVS7hoFkdA+/t6w9A5aXV9lChoBmgJaA9DCDVgkPRp93FAlIaUUpRoFUuvaBZHQPv7esCbMHN1fZQoaAZoCWgPQwhFSN3OvhhyQJSGlFKUaBVLqmgWR0D7+3tQrMC+dX2UKGgGaAloD0MIFaqbiz+RcECUhpRSlGgVS6loFkdA+/t7Q8bJfnV9lChoBmgJaA9DCKVquwk+R3JAlIaUUpRoFUvAaBZHQPv7fqo86mx1fZQoaAZoCWgPQwiESlzHOG5zQJSGlFKUaBVLvWgWR0D7+37mvW6LdX2UKGgGaAloD0MIpkV9kvsFdECUhpRSlGgVS7JoFkdA+/t/MIu5BnV9lChoBmgJaA9DCBxClZr9YXJAlIaUUpRoFUu2aBZHQPv7f6dJ8OV1fZQoaAZoCWgPQwilLhnHCOJxQJSGlFKUaBVLtGgWR0D7+4L7WuoxdX2UKGgGaAloD0MI6nWLwNiAcUCUhpRSlGgVS9FoFkdA+/uC7haTwHV9lChoBmgJaA9DCKs/wjBgrXJAlIaUUpRoFUvCaBZHQPv7gyiO/+N1fZQoaAZoCWgPQwgHflTD/txxQJSGlFKUaBVLrmgWR0D7+4R+5OJtdX2UKGgGaAloD0MIiV5GsRxdckCUhpRSlGgVS7BoFkdA+/uGg1m8NHV9lChoBmgJaA9DCH2R0Jbz/3BAlIaUUpRoFUupaBZHQPv7hsR+SbJ1fZQoaAZoCWgPQwjiBKbTOl1yQJSGlFKUaBVLt2gWR0D7+4dcZccEdX2UKGgGaAloD0MI4jsx60W6ckCUhpRSlGgVS6NoFkdA+/uIM1baAXV9lChoBmgJaA9DCI5cN6W8bXJAlIaUUpRoFUuWaBZHQPv7idCiRGN1fZQoaAZoCWgPQwivITguI95wQJSGlFKUaBVLiWgWR0D7+4nPAwfydX2UKGgGaAloD0MIOJ7PgPqscUCUhpRSlGgVS7loFkdA+/uKsCcPOXV9lChoBmgJaA9DCAbWcfwQrXJAlIaUUpRoFUuQaBZHQPv7iwV0tAd1fZQoaAZoCWgPQwi0d0ZbFW5zQJSGlFKUaBVLwWgWR0D7+4s6RyOrdX2UKGgGaAloD0MImtL6W4KFc0CUhpRSlGgVS7NoFkdA+/uL9YfW+XV9lChoBmgJaA9DCOLmVDIAL3JAlIaUUpRoFUumaBZHQPv7i+wjdHl1fZQoaAZoCWgPQwhwzR3977ByQJSGlFKUaBVLtWgWR0D7+4vk0JnhdX2UKGgGaAloD0MIXvdWJOYSdECUhpRSlGgVS7hoFkdA+/uOTLjgh3V9lChoBmgJaA9DCNMuppnuoHJAlIaUUpRoFUuaaBZHQPv7jpgDzRR1fZQoaAZoCWgPQwjd0mpIXAdzQJSGlFKUaBVLqGgWR0D7+5CDiOvMdX2UKGgGaAloD0MIYr68AHvgc0CUhpRSlGgVS75oFkdA+/uQtlEqlXV9lChoBmgJaA9DCPJ7m/7s+XJAlIaUUpRoFUucaBZHQPv7kox46fd1fZQoaAZoCWgPQwhfC3pvzDhzQJSGlFKUaBVLoGgWR0D7+5N79hqkdX2UKGgGaAloD0MID9B9ObOPcUCUhpRSlGgVS5hoFkdA+/uUKyv9tXV9lChoBmgJaA9DCPrwLEGGPXFAlIaUUpRoFU0yA2gWR0D7+5T4o7V8dX2UKGgGaAloD0MIvyuC/20ccUCUhpRSlGgVS8BoFkdA+/uWGYfGMnV9lChoBmgJaA9DCLOZQ1JLm3FAlIaUUpRoFUuzaBZHQPv7lrGBFux1fZQoaAZoCWgPQwgUBmUaDXVyQJSGlFKUaBVLq2gWR0D7+5bXqqwRdX2UKGgGaAloD0MIV5dTAuIKc0CUhpRSlGgVS7JoFkdA+/uXI3zcynV9lChoBmgJaA9DCH+HokBf6XJAlIaUUpRoFUunaBZHQPv7lyPJaJR1fZQoaAZoCWgPQwh8LH3oQuxxQJSGlFKUaBVLrmgWR0D7+5dtYB/7dX2UKGgGaAloD0MIPXyZKIJHc0CUhpRSlGgVS7loFkdA+/uYObutwXV9lChoBmgJaA9DCBMsDmc+qnNAlIaUUpRoFUvOaBZHQPv7mPH3lCF1fZQoaAZoCWgPQwiPG343HWhxQJSGlFKUaBVLrWgWR0D7+5kxaPjodX2UKGgGaAloD0MIBrth22KScECUhpRSlGgVS6RoFkdA+/uZWLYPG3V9lChoBmgJaA9DCA69xcN7MnNAlIaUUpRoFUvKaBZHQPv7mrN6gNB1fZQoaAZoCWgPQwhN9PkoI7txQJSGlFKUaBVLsGgWR0D7+5r4n4O+dX2UKGgGaAloD0MIGAXB41vcckCUhpRSlGgVS5toFkdA+/ubcUZeiXV9lChoBmgJaA9DCDm4dMx5jXNAlIaUUpRoFUuyaBZHQPv7nC9YfXB1fZQoaAZoCWgPQwjKF7SQwMZzQJSGlFKUaBVLwmgWR0D7+5ydGAkLdX2UKGgGaAloD0MIW+z2WeVxckCUhpRSlGgVS49oFkdA+/ueaDkELnV9lChoBmgJaA9DCAA6zJeX+XJAlIaUUpRoFUvAaBZHQPv7n6AAhjh1fZQoaAZoCWgPQwg3xeOi2opzQJSGlFKUaBVLxWgWR0D7+6ETisGQdX2UKGgGaAloD0MIgsmNImt2c0CUhpRSlGgVS8xoFkdA+/uhB+OOsHV9lChoBmgJaA9DCO8DkNpE+nNAlIaUUpRoFUuqaBZHQPv7oZiPQv91fZQoaAZoCWgPQwi1T8djxiJyQJSGlFKUaBVLyWgWR0D7+6JUqQRxdX2UKGgGaAloD0MI4SU49cEAckCUhpRSlGgVS6loFkdA+/uitK7I1nV9lChoBmgJaA9DCNCc9SmHYHJAlIaUUpRoFUu6aBZHQPv7ozlV94N1fZQoaAZoCWgPQwg6W0BofdByQJSGlFKUaBVLk2gWR0D7+6Mya/h3dX2UKGgGaAloD0MIWOcYkH2tckCUhpRSlGgVS6poFkdA+/ujMc2itnV9lChoBmgJaA9DCKORzyse73JAlIaUUpRoFUu3aBZHQPv7o3Imw7l1fZQoaAZoCWgPQwjgLCXLib1wQJSGlFKUaBVLsmgWR0D7+6PoqCpWdX2UKGgGaAloD0MI5WTiVkF/cECUhpRSlGgVS7toFkdA+/ukIMBp6HV9lChoBmgJaA9DCET5ghaSznBAlIaUUpRoFUuXaBZHQPv7pBxKg7J1fZQoaAZoCWgPQwg0ZhL1AnByQJSGlFKUaBVLiGgWR0D7+6RUDMePdX2UKGgGaAloD0MILqwb704+dECUhpRSlGgVS8BoFkdA+/ulOsgdO3V9lChoBmgJaA9DCLqHhO/9ZnJAlIaUUpRoFUu6aBZHQPv7pTx6OYJ1fZQoaAZoCWgPQwiM8zehkF10QJSGlFKUaBVLvWgWR0D7+6WzQu27dX2UKGgGaAloD0MIG4NOCF3rc0CUhpRSlGgVS8VoFkdA+/um792ovXV9lChoBmgJaA9DCBy0Vx8PVnNAlIaUUpRoFUvIaBZHQPv7qFwCKaZ1fZQoaAZoCWgPQwhjtmRVBKxwQJSGlFKUaBVLp2gWR0D7+6iMOPNndX2UKGgGaAloD0MIUWuad1x0cECUhpRSlGgVS5poFkdA+/up/XwsoXV9lChoBmgJaA9DCONPVDYst3JAlIaUUpRoFUvGaBZHQPv7qsQPI4l1fZQoaAZoCWgPQwhHkbWG0rpyQJSGlFKUaBVLmWgWR0D7+6uTz/ZNdX2UKGgGaAloD0MIGCE82niXc0CUhpRSlGgVS6doFkdA+/ur1zQu3HV9lChoBmgJaA9DCOSFdHjIWHFAlIaUUpRoFUunaBZHQPv7rAS13MZ1fZQoaAZoCWgPQwjZPuQtl/ZxQJSGlFKUaBVLlWgWR0D7+6w8PWhAdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 12832, "n_steps": 4096, "gamma": 0.9995, "gae_lambda": 0.99, "ent_coef": 0.005, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 512, "n_epochs": 16, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gASVwQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsBSwFLE0MEiABTAJROhZQpjAFflIWUjEgvb3B0L2NvbmRhL2xpYi9weXRob24zLjcvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuCQwIAAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEgvb3B0L2NvbmRhL2xpYi9weXRob24zLjcvc2l0ZS1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpSMHGNsb3VkcGlja2xlLmNsb3VkcGlja2xlX2Zhc3SUjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoH32UfZQoaBZoDYwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBeMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHP8mZmZmZmZqFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.15.90.1-microsoft-standard-WSL2-x86_64-with-debian-bullseye-sid # 1 SMP Fri Jan 27 02:56:13 UTC 2023", "Python": "3.7.12", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f4d71c000e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4d71c00170>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4d71c00200>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4d71c00290>", "_build": "<function ActorCriticPolicy._build at 0x7f4d71c00320>", "forward": "<function ActorCriticPolicy.forward at 0x7f4d71c003b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4d71c00440>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4d71c004d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4d71c00560>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4d71c005f0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4d71c00680>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4d71c00710>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4d71c497e0>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 64, "num_timesteps": 5046272, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1678541778993210748, "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:": "gASVyAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBS0CFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.009254400000000107, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 308, "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-5.15.90.1-microsoft-standard-WSL2-x86_64-with-debian-bullseye-sid # 1 SMP Fri Jan 27 02:56:13 UTC 2023", "Python": "3.7.12", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu117", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:eebb5f2e8b9c20b25aeb6e14375c930f53098f399925abee8f87a6d4e6531118
-size 149549
+oid sha256:fe41172f0f5fdbe5941c5c14362061b4069567e6207525e676342454a6f8d2f7
+size 149538

ppo-LunarLander-v2/data

@@ -4,20 +4,20 @@
         ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
         "__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 0x7f7dbfc9f0e0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7dbfc9f170>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7dbfc9f200>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7dbfc9f290>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f7dbfc9f320>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f7dbfc9f3b0>",
-        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f7dbfc9f440>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7dbfc9f4d0>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f7dbfc9f560>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7dbfc9f5f0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7dbfc9f680>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f7dbfc9f710>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f4d71c000e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4d71c00170>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4d71c00200>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4d71c00290>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f4d71c00320>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f4d71c003b0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f4d71c00440>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4d71c004d0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f4d71c00560>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4d71c005f0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4d71c00680>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4d71c00710>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f7dbfceaab0>"
+        "_abc_impl": "<_abc_data object at 0x7f4d71c497e0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -43,12 +43,12 @@
         "_np_random": null
     },
     "n_envs": 64,
-    "num_timesteps": 200015872,
-    "_total_timesteps": 200000000,
+    "num_timesteps": 5046272,
+    "_total_timesteps": 5000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1678409737008144743,
+    "start_time": 1678541778993210748,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -57,7 +57,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -67,24 +67,24 @@
     "_episode_num": 0,
     "use_sde": false,
     "sde_sample_freq": -1,
-    "_current_progress_remaining": -7.935999999997279e-05,
+    "_current_progress_remaining": -0.009254400000000107,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 12832,
-    "n_steps": 4096,
-    "gamma": 0.9995,
-    "gae_lambda": 0.99,
-    "ent_coef": 0.005,
+    "_n_updates": 308,
+    "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": 512,
-    "n_epochs": 16,
+    "batch_size": 64,
+    "n_epochs": 4,
     "clip_range": {
         ":type:": "<class 'function'>",
         ":serialized:": "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"

ppo-LunarLander-v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:e6b6d554ea7b7265a3eebc6d8047fc5dbd62786af25726833f425d90aff40b8d
+oid sha256:8172cf1b18b7ba35ba9b21b85c60241bd38f658b0c9d51079b2a31d4337a39dd
 size 87929

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:3820ee897feafa5e0a60f173fc95ea6888fb9f3bcfa133046a9a0e751613b7e4
+oid sha256:59ba3ba9b1ac748895c67c3d702b8615063751a1633bccaced56edb4be0e7d52
 size 43393

results.json

@@ -1 +1 @@
-{"mean_reward": 261.8004057884157, "std_reward": 70.13235192433885, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-11T02:32:46.758210"}
+{"mean_reward": 286.29576479103315, "std_reward": 18.016235761883102, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-11T15:32:06.644323"}