a2c-PandaReachDense-v2 / config.json
M331's picture
Initial commit
fd264ea
{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVRQAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMG011bHRpSW5wdXRBY3RvckNyaXRpY1BvbGljeZSTlC4=", "__module__": "stable_baselines3.common.policies", "__doc__": "\n MultiInputActorClass 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 (Tuple)\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: Uses the CombinedExtractor\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 MultiInputActorCriticPolicy.__init__ at 0x7ff82edd5ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff82edd7280>"}, "verbose": 1, "policy_kwargs": {":type:": "<class 'dict'>", ":serialized:": "gAWVowAAAAAAAAB9lCiMDGxvZ19zdGRfaW5pdJRK/v///4wKb3J0aG9faW5pdJSJjA9vcHRpbWl6ZXJfY2xhc3OUjBN0b3JjaC5vcHRpbS5ybXNwcm9wlIwHUk1TcHJvcJSTlIwQb3B0aW1pemVyX2t3YXJnc5R9lCiMBWFscGhhlEc/764UeuFHrowDZXBzlEc+5Pi1iONo8YwMd2VpZ2h0X2RlY2F5lEsAdXUu", "log_std_init": -2, "ortho_init": false, "optimizer_class": "<class 'torch.optim.rmsprop.RMSprop'>", "optimizer_kwargs": {"alpha": 0.99, "eps": 1e-05, "weight_decay": 0}}, "num_timesteps": 1000000, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1682191878876470257, "learning_rate": 0.00096, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[0.40847853 0.00671235 0.5722203 ]\n [0.40847853 0.00671235 0.5722203 ]\n [0.40847853 0.00671235 0.5722203 ]\n [0.40847853 0.00671235 0.5722203 ]]", "desired_goal": "[[-0.25807035 0.48231432 0.27057183]\n [ 0.79546404 0.9927839 -0.336952 ]\n [-1.2359997 -0.2962509 0.3570005 ]\n [-0.4968247 -0.3035137 0.9046374 ]]", "observation": "[[0.40847853 0.00671235 0.5722203 0.08018673 0.00137508 0.06249895]\n [0.40847853 0.00671235 0.5722203 0.08018673 0.00137508 0.06249895]\n [0.40847853 0.00671235 0.5722203 0.08018673 0.00137508 0.06249895]\n [0.40847853 0.00671235 0.5722203 0.08018673 0.00137508 0.06249895]]"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYEAAAAAAAAAAEBAQGUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwSFlIwBQ5R0lFKULg=="}, "_last_original_obs": {":type:": "<class 'collections.OrderedDict'>", ":serialized:": "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", "achieved_goal": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01]]", "desired_goal": "[[ 0.03309316 0.09232968 0.02299175]\n [ 0.06973044 0.10894932 0.01405802]\n [ 0.13413875 0.10366848 0.2739711 ]\n [-0.09288555 -0.02101034 0.29743275]]", "observation": "[[ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]\n [ 3.8439669e-02 -2.1944723e-12 1.9740014e-01 0.0000000e+00\n -0.0000000e+00 0.0000000e+00]]"}, "_episode_num": 0, "use_sde": true, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVHRAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIVpv/Vx158L+UhpRSlIwBbJRLMowBdJRHQKiwPegte2N1fZQoaAZoCWgPQwhX7gVmhaLsv5SGlFKUaBVLMmgWR0Cor/wyyleodX2UKGgGaAloD0MIh/4JLlYU9L+UhpRSlGgVSzJoFkdAqK+6X2M85nV9lChoBmgJaA9DCHeBkgILIPa/lIaUUpRoFUsyaBZHQKivbkfcN6R1fZQoaAZoCWgPQwhCJhk5C3vev5SGlFKUaBVLMmgWR0Cose5Lh73PdX2UKGgGaAloD0MIMuTYeobw7L+UhpRSlGgVSzJoFkdAqLGtRLsa9HV9lChoBmgJaA9DCHldv2A37Ou/lIaUUpRoFUsyaBZHQKixbIH1OCZ1fZQoaAZoCWgPQwjmB67yBILxv5SGlFKUaBVLMmgWR0CosSGIbfgrdX2UKGgGaAloD0MI/u2yX3c667+UhpRSlGgVSzJoFkdAqLOsyk9EC3V9lChoBmgJaA9DCNjUeVT8H/K/lIaUUpRoFUsyaBZHQKiza/wAlv91fZQoaAZoCWgPQwgF3zR9dkDiv5SGlFKUaBVLMmgWR0Cosyqgh8pkdX2UKGgGaAloD0MI+mGE8Ggj9b+UhpRSlGgVSzJoFkdAqLLfLaEi+3V9lChoBmgJaA9DCIfB/BUy1+i/lIaUUpRoFUsyaBZHQKi1XbB42TB1fZQoaAZoCWgPQwg+WpwxzAnzv5SGlFKUaBVLMmgWR0CotRy5iExqdX2UKGgGaAloD0MIG0ZB8Pj24L+UhpRSlGgVSzJoFkdAqLTbhDPWx3V9lChoBmgJaA9DCFfp7jobcuq/lIaUUpRoFUsyaBZHQKi0kCcwxnF1fZQoaAZoCWgPQwg6evzepr/gv5SGlFKUaBVLMmgWR0CotzyH2ys0dX2UKGgGaAloD0MIWoRiK2ha8r+UhpRSlGgVSzJoFkdAqLb7ulXRxHV9lChoBmgJaA9DCGoUkszqnem/lIaUUpRoFUsyaBZHQKi2unc+JP91fZQoaAZoCWgPQwh2wHXFjHDrv5SGlFKUaBVLMmgWR0Cotm9oWYWtdX2UKGgGaAloD0MIYvVHGAYs3r+UhpRSlGgVSzJoFkdAqLkMNH6MznV9lChoBmgJaA9DCH5xqUpbXOe/lIaUUpRoFUsyaBZHQKi4y4Nqgyx1fZQoaAZoCWgPQwiiRiHJrB7zv5SGlFKUaBVLMmgWR0CouIq6OHWSdX2UKGgGaAloD0MI1SE3ww344L+UhpRSlGgVSzJoFkdAqLg/h0hePnV9lChoBmgJaA9DCJhp+1dWGuG/lIaUUpRoFUsyaBZHQKi65JAdGRV1fZQoaAZoCWgPQwiXqrTFNT7lv5SGlFKUaBVLMmgWR0CouqMnqmj1dX2UKGgGaAloD0MILJrOTgZH7L+UhpRSlGgVSzJoFkdAqLphZ0Syt3V9lChoBmgJaA9DCFlOQukLoeO/lIaUUpRoFUsyaBZHQKi6FYg7o0R1fZQoaAZoCWgPQwgIrBxaZLv/v5SGlFKUaBVLMmgWR0Cou/BPbfxddX2UKGgGaAloD0MIVP8gkiHH7b+UhpRSlGgVSzJoFkdAqLuuipNsWXV9lChoBmgJaA9DCDo7GRwlr+2/lIaUUpRoFUsyaBZHQKi7bJoTPB11fZQoaAZoCWgPQwiloUYhyazuv5SGlFKUaBVLMmgWR0CouyC/47A+dX2UKGgGaAloD0MI8tHijGFO6b+UhpRSlGgVSzJoFkdAqL0LGtITXnV9lChoBmgJaA9DCPQ3oRABR/C/lIaUUpRoFUsyaBZHQKi8yUSIxg11fZQoaAZoCWgPQwh1O/vKgzTyv5SGlFKUaBVLMmgWR0CovIdzXBgvdX2UKGgGaAloD0MIa2YtBaR96L+UhpRSlGgVSzJoFkdAqLw7VFx4p3V9lChoBmgJaA9DCMHkRpG1huu/lIaUUpRoFUsyaBZHQKi+DyGSIP91fZQoaAZoCWgPQwiESlzHuGLvv5SGlFKUaBVLMmgWR0Covc2nCO3ldX2UKGgGaAloD0MIdNL7xtce7b+UhpRSlGgVSzJoFkdAqL2L1CgK4XV9lChoBmgJaA9DCM6KqIk+n+q/lIaUUpRoFUsyaBZHQKi9P7tzCDV1fZQoaAZoCWgPQwhsBrggWxbjv5SGlFKUaBVLMmgWR0Covy+RPoFFdX2UKGgGaAloD0MIuWx0zk9x4r+UhpRSlGgVSzJoFkdAqL7t6C17Y3V9lChoBmgJaA9DCJC8cyhD1em/lIaUUpRoFUsyaBZHQKi+rBBzFMt1fZQoaAZoCWgPQwjpLLMIxVblv5SGlFKUaBVLMmgWR0CovmAd4mkWdX2UKGgGaAloD0MI3C3JAbua5L+UhpRSlGgVSzJoFkdAqMBTLhaTwHV9lChoBmgJaA9DCMPUljrIa+2/lIaUUpRoFUsyaBZHQKjAEZFXq7l1fZQoaAZoCWgPQwjWj03yI/7hv5SGlFKUaBVLMmgWR0Cov8/LLZBcdX2UKGgGaAloD0MIK21xjc9k4r+UhpRSlGgVSzJoFkdAqL+D1GsmwHV9lChoBmgJaA9DCAUx0LUvIPC/lIaUUpRoFUsyaBZHQKjBeFzuF6B1fZQoaAZoCWgPQwhnX3mQniLjv5SGlFKUaBVLMmgWR0CowTbILgGbdX2UKGgGaAloD0MIdmuZDMfz47+UhpRSlGgVSzJoFkdAqMD07Sy+pXV9lChoBmgJaA9DCNnsSPWdX9e/lIaUUpRoFUsyaBZHQKjAqNrj5sV1fZQoaAZoCWgPQwiGkPP+P07gv5SGlFKUaBVLMmgWR0CowpoFV1fWdX2UKGgGaAloD0MI0uEhjJ/G1b+UhpRSlGgVSzJoFkdAqMJYbuMMqnV9lChoBmgJaA9DCFxWYTPABeC/lIaUUpRoFUsyaBZHQKjCFpGnXNF1fZQoaAZoCWgPQwjzx7Q2je3mv5SGlFKUaBVLMmgWR0CowcqaG5+ZdX2UKGgGaAloD0MIFvvL7slD6b+UhpRSlGgVSzJoFkdAqMOm1D0Dl3V9lChoBmgJaA9DCBvYKsHicOe/lIaUUpRoFUsyaBZHQKjDZRVp9JB1fZQoaAZoCWgPQwjk1qTbErnwv5SGlFKUaBVLMmgWR0CowyMjVx0ddX2UKGgGaAloD0MIpKt0d52N6r+UhpRSlGgVSzJoFkdAqMLXE87p3XV9lChoBmgJaA9DCGtgqwSLQ+G/lIaUUpRoFUsyaBZHQKjEvIEr5Ip1fZQoaAZoCWgPQwidLouJzUfgv5SGlFKUaBVLMmgWR0CoxHruYx+KdX2UKGgGaAloD0MIX5uNlZjn57+UhpRSlGgVSzJoFkdAqMQ5E0BOpXV9lChoBmgJaA9DCE+TGW8rve+/lIaUUpRoFUsyaBZHQKjD7OeJ53V1fZQoaAZoCWgPQwiRK/UsCOXkv5SGlFKUaBVLMmgWR0CoxcUrK/21dX2UKGgGaAloD0MI00z3Oqmv5L+UhpRSlGgVSzJoFkdAqMWDND+irXV9lChoBmgJaA9DCM+ey9Qk+Oi/lIaUUpRoFUsyaBZHQKjFQRWcSXd1fZQoaAZoCWgPQwjAzeLFwhDhv5SGlFKUaBVLMmgWR0CoxPUBXCCSdX2UKGgGaAloD0MIR//LtWgB47+UhpRSlGgVSzJoFkdAqMbbjHXEqHV9lChoBmgJaA9DCBps6jwq/uS/lIaUUpRoFUsyaBZHQKjGmh4dIXl1fZQoaAZoCWgPQwiFfNCzWbXxv5SGlFKUaBVLMmgWR0CoxlhU70WedX2UKGgGaAloD0MIUz4EVaPX5L+UhpRSlGgVSzJoFkdAqMYMVk+X7nV9lChoBmgJaA9DCGa+g584gOS/lIaUUpRoFUsyaBZHQKjH7QWvbGp1fZQoaAZoCWgPQwi06QjgZnHpv5SGlFKUaBVLMmgWR0Cox6tnf2sadX2UKGgGaAloD0MIHzF6bqEr4r+UhpRSlGgVSzJoFkdAqMdpiobXH3V9lChoBmgJaA9DCGaDTDJyFuy/lIaUUpRoFUsyaBZHQKjHHW8yvcJ1fZQoaAZoCWgPQwgRUrezrzztv5SGlFKUaBVLMmgWR0CoyP1ghKUWdX2UKGgGaAloD0MIiEfi5elc5L+UhpRSlGgVSzJoFkdAqMi7aK1og3V9lChoBmgJaA9DCLcNoyB4fNC/lIaUUpRoFUsyaBZHQKjIeXQdCE91fZQoaAZoCWgPQwidDmQ9tfrUv5SGlFKUaBVLMmgWR0CoyC1hb4ahdX2UKGgGaAloD0MISdv4E5UN4L+UhpRSlGgVSzJoFkdAqMoTw8W9DnV9lChoBmgJaA9DCHO6LCY2H96/lIaUUpRoFUsyaBZHQKjJ0iGnGbV1fZQoaAZoCWgPQwjq6SPwh5/lv5SGlFKUaBVLMmgWR0CoyZAOSW7fdX2UKGgGaAloD0MIIjgu46ZG8L+UhpRSlGgVSzJoFkdAqMlEG7jDK3V9lChoBmgJaA9DCGppboWwGuO/lIaUUpRoFUsyaBZHQKjLJaA4GUx1fZQoaAZoCWgPQwgvvmiPF9Lkv5SGlFKUaBVLMmgWR0CoyuPGyX2NdX2UKGgGaAloD0MIwsO0b+6v4L+UhpRSlGgVSzJoFkdAqMqh9Tgl4XV9lChoBmgJaA9DCEG4Agr19PK/lIaUUpRoFUsyaBZHQKjKVglWwNd1fZQoaAZoCWgPQwhYVwVqMXjrv5SGlFKUaBVLMmgWR0CozDaRyOrAdX2UKGgGaAloD0MI+GuyRj1E6r+UhpRSlGgVSzJoFkdAqMv09W6shnV9lChoBmgJaA9DCG6JXHAGf9m/lIaUUpRoFUsyaBZHQKjLsxk/bCd1fZQoaAZoCWgPQwgt7dRcbrDlv5SGlFKUaBVLMmgWR0Coy2cMmWt2dX2UKGgGaAloD0MIzsKedvhr27+UhpRSlGgVSzJoFkdAqM1aRdQfp3V9lChoBmgJaA9DCOi9MQQAx9q/lIaUUpRoFUsyaBZHQKjNGJ7b+Lp1fZQoaAZoCWgPQwjDgvsBD4zhv5SGlFKUaBVLMmgWR0CozNaY/mkndX2UKGgGaAloD0MIRdREn48y6L+UhpRSlGgVSzJoFkdAqMyKji4rjHV9lChoBmgJaA9DCAot6/6xEN+/lIaUUpRoFUsyaBZHQKjObMC9ytF1fZQoaAZoCWgPQwgNchdhinLVv5SGlFKUaBVLMmgWR0CozirX18LKdX2UKGgGaAloD0MIER0CRwLN87+UhpRSlGgVSzJoFkdAqM3o4jrzG3V9lChoBmgJaA9DCDbK+s3E9Oq/lIaUUpRoFUsyaBZHQKjNnMwDeTF1ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 31250, "n_steps": 8, "gamma": 0.99, "gae_lambda": 0.9, "ent_coef": 0.0, "vf_coef": 0.4, "max_grad_norm": 0.5, "normalize_advantage": false, "observation_space": {":type:": "<class 'gym.spaces.dict.Dict'>", ":serialized:": "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", "spaces": "OrderedDict([('achieved_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('desired_goal', Box([-10. -10. -10.], [10. 10. 10.], (3,), float32)), ('observation', Box([-10. -10. -10. -10. -10. -10.], [10. 10. 10. 10. 10. 10.], (6,), float32))])", "_shape": null, "dtype": null, "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [3], "low": "[-1. -1. -1.]", "high": "[1. 1. 1.]", "bounded_below": "[ True True True]", "bounded_above": "[ True True True]", "_np_random": null}, "n_envs": 4, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.8.0", "PyTorch": "2.0.0+cu118", "GPU Enabled": "True", "Numpy": "1.22.4", "Gym": "0.21.0"}}