Upload PPO LunarLander-v2 trained agent

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -1817.44 +/- 434.47
+      value: -68.97 +/- 138.26
       name: mean_reward
     task:
       type: reinforcement-learning
@@ -20,3 +20,4 @@ model-index:
       type: LunarLander-v2
 ---
 
+

config.json

@@ -1 +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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x000002444D3F8828>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000002444D3F88B8>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000002444D3F8948>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000002444D3F89D8>", "_build": "<function ActorCriticPolicy._build at 0x000002444D3F8A68>", "forward": "<function ActorCriticPolicy.forward at 0x000002444D3F8AF8>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000002444D3F8B88>", "_predict": "<function ActorCriticPolicy._predict at 0x000002444D3F8C18>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000002444D3F8CA8>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000002444D3F8D38>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x000002444D3F8DC8>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x000002444D3DBED0>"}, "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": 1, "num_timesteps": 10240, "_total_timesteps": 10000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652241242.8191128, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAA1paL5eCa4+7nZvvp5Bhr9Hm0i+JelZvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.02400000000000002, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIaCPXTSluXMCUhpRSlIwBbJRLT4wBdJRHP+sptJnQID51fZQoaAZoCWgPQwgRjln2JFhvwJSGlFKUaBVLmWgWRz/xOvMbFS88dX2UKGgGaAloD0MIBCDu6lVjW8CUhpRSlGgVS09oFkc/8vVrhzeXRnV9lChoBmgJaA9DCE7wTdPnK3LAlIaUUpRoFUtqaBZHP/VTw2ETQE91fZQoaAZoCWgPQwjSOT/F8dZuwJSGlFKUaBVLZGgWRz/3gPI4lyBDdX2UKGgGaAloD0MIMA+Z8iFzUMCUhpRSlGgVS0NoFkc/+PGrCFbml3V9lChoBmgJaA9DCPXabKxEf3nAlIaUUpRoFUttaBZHP/t04zabnYB1fZQoaAZoCWgPQwj2twTgn6VbwJSGlFKUaBVLcGgWRz/957b+Lm6odX2UKGgGaAloD0MIE51lFqEKT8CUhpRSlGgVS0FoFkc//1A+pwS8J3V9lChoBmgJaA9DCGt9kdAWlW3AlIaUUpRoFUtLaBZHQAB2bG3nZCh1fZQoaAZoCWgPQwh3vwrwXS9lwJSGlFKUaBVLWGgWR0ABbD0lJHy3dX2UKGgGaAloD0MI+KV+3tRMdMCUhpRSlGgVS2toFkdAApVENOM2nHV9lChoBmgJaA9DCK0Yrg4ApmPAlIaUUpRoFUtcaBZHQAOVUuL74zt1fZQoaAZoCWgPQwhcPpKSHuROwJSGlFKUaBVLPmgWR0AEQ3FUADJVdX2UKGgGaAloD0MI0el5N5Y8YsCUhpRSlGgVS0FoFkdABPe1KGtZFHV9lChoBmgJaA9DCM9J7xufoHDAlIaUUpRoFUtkaBZHQAYgvDgqEvl1fZQoaAZoCWgPQwjay7bT1vhxwJSGlFKUaBVLW2gWR0AHIMrmQr+YdX2UKGgGaAloD0MInbryWZ5/ZcCUhpRSlGgVS31oFkdAEThnanJkoXV9lChoBmgJaA9DCDVeukkMO13AlIaUUpRoFUtLaBZHQBGGPtD2Jzl1fZQoaAZoCWgPQwg+z582qthjwJSGlFKUaBVLVGgWR0AR3Efkmx+sdX2UKGgGaAloD0MIcNBefTyPX8CUhpRSlGgVSz5oFkdAEhzOoo/iYXV9lChoBmgJaA9DCCEf9GxWyFPAlIaUUpRoFUt9aBZHQBKgFs54nnd1fZQoaAZoCWgPQwhOmgZFc1pswJSGlFKUaBVLVWgWR0AS/1f3N9pidX2UKGgGaAloD0MIj6hQ3VwHWcCUhpRSlGgVS1xoFkdAE2fQrtmcv3V9lChoBmgJaA9DCLdj6q7sb1PAlIaUUpRoFUtYaBZHQBPGCyyD7Il1fZQoaAZoCWgPQwjtZdtp65FvwJSGlFKUaBVLd2gWR0AUQwAU+LWJdX2UKGgGaAloD0MInx1wXTEhW8CUhpRSlGgVS2hoFkdAFK+RoysS03V9lChoBmgJaA9DCDquRnalt1nAlIaUUpRoFUt5aBZHQBUulj3Ehq11fZQoaAZoCWgPQwhl/WZiumg8wJSGlFKUaBVLiWgWR0AVwQnQY1pCdX2UKGgGaAloD0MIVKnZA+0ce8CUhpRSlGgVS39oFkdAFlJ6po9LYnV9lChoBmgJaA9DCGDNAYK5oGjAlIaUUpRoFUt4aBZHQBbUjopx3mp1fZQoaAZoCWgPQwj9EBssnKBcwJSGlFKUaBVLUmgWR0AXKZE2Hck/dX2UKGgGaAloD0MI9gg1QypebMCUhpRSlGgVS4VoFkdAF7aY/mknC3V9lChoBmgJaA9DCEDCMGDJH0nAlIaUUpRoFUtvaBZHQBgxgVoHs1N1fZQoaAZoCWgPQwihuyTOCstqwJSGlFKUaBVLS2gWR0AYhHe7+T/ydX2UKGgGaAloD0MIcvkP6bfCXMCUhpRSlGgVS0FoFkdAGMwqAjIJaHV9lChoBmgJaA9DCJayDHGsb2vAlIaUUpRoFUtkaBZHQBk5wbVBlc11fZQoaAZoCWgPQwizmNh8XC1cwJSGlFKUaBVLRGgWR0AZiJ9Aood/dX2UKGgGaAloD0MIk6ZB0TwOacCUhpRSlGgVS1BoFkdAHqyP+4smOXV9lChoBmgJaA9DCEwz3eukiF3AlIaUUpRoFUtWaBZHQB8P7aZhKDl1fZQoaAZoCWgPQwhNTYI3pNFhwJSGlFKUaBVLZ2gWR0AfgqAjIJZ4dX2UKGgGaAloD0MI5X/ydy/DdMCUhpRSlGgVS2BoFkdAH+4rz5GjK3V9lChoBmgJaA9DCCMva2LB7HHAlIaUUpRoFUudaBZHQCBQtL+PzWh1fZQoaAZoCWgPQwjS/gdYqzVawJSGlFKUaBVLZWgWR0AgiQumJm/WdX2UKGgGaAloD0MI3PXSFAFZZMCUhpRSlGgVS2JoFkdAIL/VqesgdXV9lChoBmgJaA9DCJepSfCGXFHAlIaUUpRoFUtFaBZHQCDluxbB42V1fZQoaAZoCWgPQwjuBWaFogdjwJSGlFKUaBVLcmgWR0AhJb7j1f3OdX2UKGgGaAloD0MI6MHdWXtRcMCUhpRSlGgVS2BoFkdAIVy+Yc/+sHV9lChoBmgJaA9DCC4gtB6+rlnAlIaUUpRoFUs/aBZHQCGAl4TsY2t1fZQoaAZoCWgPQwjPS8XGvAhawJSGlFKUaBVLRmgWR0Ahp4M4LkS3dX2UKGgGaAloD0MII/Qz9brWXcCUhpRSlGgVS2toFkdAIeNuLrHEM3V9lChoBmgJaA9DCEnYt5OIsVrAlIaUUpRoFUtIaBZHQCIL4xk/bCd1fZQoaAZoCWgPQwg/5C1XP/hSwJSGlFKUaBVLTmgWR0AiN2rXDm8vdX2UKGgGaAloD0MIM9/BTxzGTcCUhpRSlGgVS0VoFkdAImBi9Zid8XV9lChoBmgJaA9DCJPi4xMyt2rAlIaUUpRoFUtJaBZHQCKL6pHZsbh1fZQoaAZoCWgPQwhw626e6hJfwJSGlFKUaBVLSWgWR0Ait/VAiV0LdX2UKGgGaAloD0MImN9pMuO8aMCUhpRSlGgVS3BoFkdAIv2bG3nZCnV9lChoBmgJaA9DCDj3V4/7rVXAlIaUUpRoFUuEaBZHQCNPi1iONo91fZQoaAZoCWgPQwjDt7BuvPtdwJSGlFKUaBVLUmgWR0AjhM3ZPEbYdX2UKGgGaAloD0MI7ImuCz9ZWsCUhpRSlGgVS01oFkdAI7TxXnyNGXV9lChoBmgJaA9DCATltn0PUmDAlIaUUpRoFUtGaBZHQCPgeRxLkCF1fZQoaAZoCWgPQwhV3LjF/OVZwJSGlFKUaBVLWGgWR0Amc+vhZQpGdX2UKGgGaAloD0MItvXTf9ZBY8CUhpRSlGgVS1loFkdAJqUVrRBu43V9lChoBmgJaA9DCB8Svvc37VTAlIaUUpRoFUuaaBZHQCb8eZG8VYZ1fZQoaAZoCWgPQwhJ88e0tv1kwJSGlFKUaBVLg2gWR0AnRTH80k4WdX2UKGgGaAloD0MIzcr2Ie9TYsCUhpRSlGgVS3RoFkdAJ4jLbHp8nnV9lChoBmgJaA9DCIOHad/crFnAlIaUUpRoFUtDaBZHQCetqrR0EHN1fZQoaAZoCWgPQwjbNSGtMWhDwJSGlFKUaBVLc2gWR0An7jFyaNModX2UKGgGaAloD0MI4UGz614KYMCUhpRSlGgVS4loFkdAKDtpM6BAfXV9lChoBmgJaA9DCCpVouwtkWHAlIaUUpRoFUtqaBZHQCh2TeO4oZ11fZQoaAZoCWgPQwgptKz7h7JywJSGlFKUaBVLn2gWR0Aoz+yZ8a4udX2UKGgGaAloD0MIvMywUdYhW8CUhpRSlGgVS1BoFkdAKPv3ai9Iw3V9lChoBmgJaA9DCMgoz7ycTWPAlIaUUpRoFUtbaBZHQCkxOerdWQx1fZQoaAZoCWgPQwhmTpfFxBlWwJSGlFKUaBVLcGgWR0Apd9roGIKudX2UKGgGaAloD0MIBOeMKO3ybcCUhpRSlGgVS2BoFkdAKbI8hcJMQHV9lChoBmgJaA9DCDVfJR+7xlvAlIaUUpRoFUtQaBZHQCniYAsCkoF1fZQoaAZoCWgPQwh1sWmlENFWwJSGlFKUaBVLT2gWR0AqEwbEP1+RdX2UKGgGaAloD0MImwEuyJZhUMCUhpRSlGgVS5poFkdAKnRT850bLnV9lChoBmgJaA9DCG+9pgcFPVHAlIaUUpRoFUtOaBZHQCqj9GZuyeJ1fZQoaAZoCWgPQwjE0VW6u144wJSGlFKUaBVLeWgWR0Aq8EKVpsXSdX2UKGgGaAloD0MI2qhOB7IcTcCUhpRSlGgVS3doFkdALZc1wYLsr3V9lChoBmgJaA9DCA/R6A5ir1fAlIaUUpRoFUuRaBZHQC3qLCN0eU91fZQoaAZoCWgPQwh7vma5bJ5EwJSGlFKUaBVLT2gWR0AuFjbSJCSidX2UKGgGaAloD0MIgA2IEFcuWcCUhpRSlGgVS1VoFkdALkjp9qk/KXV9lChoBmgJaA9DCL4yb9V1AkvAlIaUUpRoFUuDaBZHQC6eb/ffoA51fZQoaAZoCWgPQwiCdRw/VCJNwJSGlFKUaBVLT2gWR0Au0SL61stTdX2UKGgGaAloD0MInDHMCdpAS8CUhpRSlGgVS2RoFkdALwloUSIxg3V9lChoBmgJaA9DCMmvH2KDqGLAlIaUUpRoFUt8aBZHQC9QFC9h7Vt1fZQoaAZoCWgPQwg0gSIWMYZPwJSGlFKUaBVLUWgWR0AvgkRBeHBUdX2UKGgGaAloD0MIv30dOGeSY8CUhpRSlGgVS2ZoFkdAL76ySmqHXXV9lChoBmgJaA9DCMstrYbEulLAlIaUUpRoFUtIaBZHQC/osI3R5Tt1fZQoaAZoCWgPQwhYVS+/0yZPwJSGlFKUaBVLSWgWR0AwChwVCXyBdX2UKGgGaAloD0MIFY4glWJvOcCUhpRSlGgVS1hoFkdAMCQ/oq0+knV9lChoBmgJaA9DCG9IowIn5lrAlIaUUpRoFUtiaBZHQDBB85S3sol1fZQoaAZoCWgPQwi6SQwCK5ZYwJSGlFKUaBVLVGgWR0AwX+jua4MGdX2UKGgGaAloD0MIUYiAQ6i2QcCUhpRSlGgVS0doFkdAMHlCgK4QSXV9lChoBmgJaA9DCN0Gtd9aUmDAlIaUUpRoFUthaBZHQDCZhd+ocaR1fZQoaAZoCWgPQwgQecvVj3dAQJSGlFKUaBVLkGgWR0Awxthd+ocadX2UKGgGaAloD0MIfotOltp5b8CUhpRSlGgVS3poFkdAMOu3pfQa73V9lChoBmgJaA9DCPZhvVErPV/AlIaUUpRoFUtmaBZHQDEKuW8h9st1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 50, "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, "system_info": {"OS": "Windows-10-10.0.19041-SP0 10.0.19041", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.21.5", "Gym": "0.21.0"}}
+{"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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x000001FDA709CE58>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001FDA709CEE8>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001FDA709CF78>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001FDA70A3048>", "_build": "<function ActorCriticPolicy._build at 0x000001FDA70A30D8>", "forward": "<function ActorCriticPolicy.forward at 0x000001FDA70A3168>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001FDA70A31F8>", "_predict": "<function ActorCriticPolicy._predict at 0x000001FDA70A3288>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001FDA70A3318>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001FDA70A33A8>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001FDA70A3438>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x000001FDA7085EA0>"}, "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": 1, "num_timesteps": 5120, "_total_timesteps": 5000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652241565.248278, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAABL3YL9nln8/eSvEv0+cg79AVLk/oqv3PgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.02400000000000002, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 20, "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": "Windows-10-10.0.19041-SP0 10.0.19041", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0", "GPU Enabled": "True", "Numpy": "1.21.5", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:1f5b49b0c7480d46303897133c1d5ddab027c0f20df7cd4d58aa4005424647bd
-size 143126
+oid sha256:7b22549247377026bac13d529ea2d58b36b3ff52ec297955a641856c8ef495e7
+size 140672

ppo-LunarLander-v2/data

@@ -4,19 +4,19 @@
         ":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 sde_net_arch: Network architecture for extracting features\n        when using gSDE. If None, the latent features from the policy will be used.\n        Pass an empty list to use the states as features.\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 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 0x000002444D3F8828>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000002444D3F88B8>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000002444D3F8948>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000002444D3F89D8>",
-        "_build": "<function ActorCriticPolicy._build at 0x000002444D3F8A68>",
-        "forward": "<function ActorCriticPolicy.forward at 0x000002444D3F8AF8>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000002444D3F8B88>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x000002444D3F8C18>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000002444D3F8CA8>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000002444D3F8D38>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x000002444D3F8DC8>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x000001FDA709CE58>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x000001FDA709CEE8>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x000001FDA709CF78>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x000001FDA70A3048>",
+        "_build": "<function ActorCriticPolicy._build at 0x000001FDA70A30D8>",
+        "forward": "<function ActorCriticPolicy.forward at 0x000001FDA70A3168>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x000001FDA70A31F8>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x000001FDA70A3288>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x000001FDA70A3318>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x000001FDA70A33A8>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x000001FDA70A3438>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x000002444D3DBED0>"
+        "_abc_impl": "<_abc_data object at 0x000001FDA7085EA0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -42,12 +42,12 @@
         "_np_random": null
     },
     "n_envs": 1,
-    "num_timesteps": 10240,
-    "_total_timesteps": 10000,
+    "num_timesteps": 5120,
+    "_total_timesteps": 5000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1652241242.8191128,
+    "start_time": 1652241565.248278,
     "learning_rate": 0.0003,
     "tensorboard_log": null,
     "lr_schedule": {
@@ -56,7 +56,7 @@
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
-        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAA1paL5eCa4+7nZvvp5Bhr9Hm0i+JelZvgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
+        ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAABL3YL9nln8/eSvEv0+cg79AVLk/oqv3PgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
     },
     "_last_episode_starts": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -69,21 +69,21 @@
     "_current_progress_remaining": -0.02400000000000002,
     "ep_info_buffer": {
         ":type:": "<class 'collections.deque'>",
-        ":serialized:": "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"
+        ":serialized:": "gAWV7QgAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIZcdGIF4SY8CUhpRSlIwBbJRLcowBdJRHQDtSRB/qgRN1fZQoaAZoCWgPQwjGGFjH8atfwJSGlFKUaBVLPGgWR0A7aU+s5n14dX2UKGgGaAloD0MIZLDiVOvoZMCUhpRSlGgVS4BoFkdAO5cqOLiuMnV9lChoBmgJaA9DCDRKl/4lKXzAlIaUUpRoFUtZaBZHQDu3rzGxUvR1fZQoaAZoCWgPQwju0LAYdWBVwJSGlFKUaBVLQGgWR0A7zz3h4t6HdX2UKGgGaAloD0MIYp0q3zNTaMCUhpRSlGgVS0RoFkdAO+iXY150KnV9lChoBmgJaA9DCFkUdlF0kHDAlIaUUpRoFUtzaBZHQDwTm+0w8GN1fZQoaAZoCWgPQwifyf55GllmwJSGlFKUaBVLdmgWR0A8PNrj5sTGdX2UKGgGaAloD0MIBeCfUmXacsCUhpRSlGgVS25oFkdAPGOFDfFaS3V9lChoBmgJaA9DCMu9wKxQd2zAlIaUUpRoFUuBaBZHQDySYNRWLgp1fZQoaAZoCWgPQwhGXAAaJYJpwJSGlFKUaBVLeGgWR0A8/rDZUT+OdX2UKGgGaAloD0MIb9dLUwRcYMCUhpRSlGgVS35oFkdAPSRUvPC2t3V9lChoBmgJaA9DCNL7xteeCF3AlIaUUpRoFUtdaBZHQD1APczqKP51fZQoaAZoCWgPQwjHZdzUQFVkwJSGlFKUaBVLUWgWR0A9WJEpiI+GdX2UKGgGaAloD0MIZR2OrtIcXcCUhpRSlGgVS01oFkdAPW9bLU1AJXV9lChoBmgJaA9DCLCRJAhXnlnAlIaUUpRoFUtRaBZHQD2HmV7hNud1fZQoaAZoCWgPQwhnYroQaw9zwJSGlFKUaBVLdGgWR0A9qmw7kn1GdX2UKGgGaAloD0MIZw+0AkNXYcCUhpRSlGgVS1FoFkdAPcK/mDDjznV9lChoBmgJaA9DCG9m9KNhgGLAlIaUUpRoFUtOaBZHQD3ZyyUs4DN1fZQoaAZoCWgPQwiZSdQLPkFYwJSGlFKUaBVLS2gWR0A98h5xBE8adX2UKGgGaAloD0MIMsueBDZTcsCUhpRSlGgVS11oFkdAPg4HgP3BYXV9lChoBmgJaA9DCG7eOCnM52PAlIaUUpRoFUtQaBZHQD5nZK3/gix1fZQoaAZoCWgPQwjBpzl5kRVVwJSGlFKUaBVLRWgWR0A+e580DU3GdX2UKGgGaAloD0MIT8x6MZR2XsCUhpRSlGgVS1hoFkdAPpWKMvRJE3V9lChoBmgJaA9DCAndJXFW6FrAlIaUUpRoFUtPaBZHQD6tGOMl1KZ1fZQoaAZoCWgPQwjtKw/SU0lUwJSGlFKUaBVLdWgWR0A+z6o2n88+dX2UKGgGaAloD0MI4JwRpb02ZsCUhpRSlGgVSzdoFkdAPt/2TPjXF3V9lChoBmgJaA9DCP+uz5z1Ol3AlIaUUpRoFUs+aBZHQD7zKp1ie/Z1fZQoaAZoCWgPQwjhQ4mWPBBzwJSGlFKUaBVLlmgWR0A/IDuSfUWmdX2UKGgGaAloD0MIsqAwKNPRZ8CUhpRSlGgVS0BoFkdAPzMuSOinHnV9lChoBmgJaA9DCCekNQZdaHDAlIaUUpRoFUuCaBZHQD9Z2IO6NER1fZQoaAZoCWgPQwj6KY4Dr0dcwJSGlFKUaBVLQWgWR0A/bMs6JZW8dX2UKGgGaAloD0MImngHeFI/eMCUhpRSlGgVS3doFkdAP9XHFPznR3V9lChoBmgJaA9DCIyeW+hK9FnAlIaUUpRoFUtDaBZHQD/pfnfVI7N1fZQoaAZoCWgPQwjp81FG3E9owJSGlFKUaBVLhGgWR0BAB7H6uW8idX2UKGgGaAloD0MIJv4o6sxxYMCUhpRSlGgVS3loFkdAQBi5I6KceHV9lChoBmgJaA9DCH3PSIRGDl3AlIaUUpRoFUtMaBZHQEAjWXkYGdJ1fZQoaAZoCWgPQwh002achuZbwJSGlFKUaBVLQGgWR0BALE/B3zMBdX2UKGgGaAloD0MIv4I0Y9G+XcCUhpRSlGgVS0doFkdAQDYrc0tRN3V9lChoBmgJaA9DCNAKDFndilnAlIaUUpRoFUtbaBZHQEBC+49X9zh1fZQoaAZoCWgPQwhxAtNpXRlgwJSGlFKUaBVLb2gWR0BAUrrxAjY7dX2UKGgGaAloD0MIMshdhCmuW8CUhpRSlGgVS1poFkdAQF9nscABDHV9lChoBmgJaA9DCK9Cyk+q91jAlIaUUpRoFUtXaBZHQEBrkVeruIB1fZQoaAZoCWgPQwiAn3HhQA5VwJSGlFKUaBVLSmgWR0BAdlJ6IFeOdX2UKGgGaAloD0MI4e8XsyUSW8CUhpRSlGgVS1FoFkdAQKBjpcHGCXV9lChoBmgJaA9DCIs3Mo/87l/AlIaUUpRoFUtfaBZHQECttE5Qxet1fZQoaAZoCWgPQwhZ3lUPmDFXwJSGlFKUaBVLcWgWR0BAvbU5MlC1dX2UKGgGaAloD0MITN2VXTA7ZcCUhpRSlGgVS4BoFkdAQM/Ye1a4c3V9lChoBmgJaA9DCPInKhvWdlnAlIaUUpRoFUtQaBZHQEDbPX05EMN1fZQoaAZoCWgPQwjNVl7yvzNgwJSGlFKUaBVLTmgWR0BA5oG6f8MvdX2UKGgGaAloD0MIJJhqZi1fasCUhpRSlGgVS2ZoFkdAQPW+AVfu1HV9lChoBmgJaA9DCKhSswdanF/AlIaUUpRoFUt0aBZHQEEH7BwdbPh1fZQoaAZoCWgPQwjnilJCMPRiwJSGlFKUaBVLQGgWR0BBEu7HyVfNdX2UKGgGaAloD0MIzhjmBC1gc8CUhpRSlGgVS2JoFkdAQSKuKXOW0XVlLg=="
     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
         ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
     },
-    "_n_updates": 50,
-    "n_steps": 2048,
-    "gamma": 0.99,
-    "gae_lambda": 0.95,
-    "ent_coef": 0.0,
+    "_n_updates": 20,
+    "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": 10,
+    "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 sha256:3c91424d1ec4bb1e9e5c281b23555c1f29735a2d33be1dcb0bbac7f2447193d8
+oid sha256:d01df8ecb6989cd28b20e2210c31e7289dbddba6c96897d9dc8337aefe799885
 size 84829

ppo-LunarLander-v2/policy.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:046f7c344bddefe4204d16d0e825c9e4c9befedd0a6680f605ba5ed002fb42e7
+oid sha256:5b51feb7ff107ddad3f86252d1ae15cfeee96f4c7523651d6d9f98eb0b53e8c2
 size 43201

results.json

@@ -1 +1 @@
-{"mean_reward": -1817.444702244969, "std_reward": 434.47063870172576, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-11T15:56:39.766400"}
+{"mean_reward": -68.97254847993608, "std_reward": 138.26151613287536, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-11T16:02:40.380810"}