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tqc-HalfCheetah-v3-308614917

Reinforcement Learning
stable-baselines3
HalfCheetah-v3
deep-reinforcement-learning
Eval Results
Model card Files Community
tqc-HalfCheetah-v3-308614917
File size: 19,455 Bytes 
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{
    "policy_class": {
        ":type:": "<class 'abc.ABCMeta'>",
        ":serialized:": "gAWVKgAAAAAAAACMGHNiM19jb250cmliLnRxYy5wb2xpY2llc5SMCVRRQ1BvbGljeZSTlC4=",
        "__module__": "sb3_contrib.tqc.policies",
        "__doc__": "\n    Policy class (with both actor and critic) for TQC.\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 use_sde: Whether to use State Dependent Exploration or not\n    :param log_std_init: Initial value for the log standard deviation\n    :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\n    :param features_extractor_class: Features extractor to use.\n    :param features_extractor_kwargs: Keyword arguments\n        to pass to the feature 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    :param n_quantiles: Number of quantiles for the critic.\n    :param n_critics: Number of critic networks to create.\n    :param share_features_extractor: Whether to share or not the features extractor\n        between the actor and the critic (this saves computation time)\n    ",
        "__init__": "<function TQCPolicy.__init__ at 0x7fd9eb966670>",
        "_build": "<function TQCPolicy._build at 0x7fd9eb966700>",
        "_get_constructor_parameters": "<function TQCPolicy._get_constructor_parameters at 0x7fd9eb966790>",
        "reset_noise": "<function TQCPolicy.reset_noise at 0x7fd9eb966820>",
        "make_actor": "<function TQCPolicy.make_actor at 0x7fd9eb9668b0>",
        "make_critic": "<function TQCPolicy.make_critic at 0x7fd9eb966940>",
        "forward": "<function TQCPolicy.forward at 0x7fd9eb9669d0>",
        "_predict": "<function TQCPolicy._predict at 0x7fd9eb966a60>",
        "set_training_mode": "<function TQCPolicy.set_training_mode at 0x7fd9eb966af0>",
        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7fd9eb9690c0>"
    },
    "verbose": 1,
    "policy_kwargs": {
        "use_sde": false
    },
    "observation_space": {
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        "dtype": "float64",
        "_shape": [
            17
        ],
        "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf]",
        "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf]",
        "bounded_below": "[False False False False False False False False False False False False\n False False False False False]",
        "bounded_above": "[False False False False False False False False False False False False\n False False False False False]",
        "_np_random": null
    },
    "action_space": {
        ":type:": "<class 'gym.spaces.box.Box'>",
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        "dtype": "float32",
        "_shape": [
            6
        ],
        "low": "[-1. -1. -1. -1. -1. -1.]",
        "high": "[1. 1. 1. 1. 1. 1.]",
        "bounded_below": "[ True  True  True  True  True  True]",
        "bounded_above": "[ True  True  True  True  True  True]",
        "_np_random": "RandomState(MT19937)"
    },
    "n_envs": 1,
    "num_timesteps": 1000000,
    "_total_timesteps": 1000000,
    "_num_timesteps_at_start": 0,
    "seed": 0,
    "action_noise": null,
    "start_time": 1676039143122195813,
    "learning_rate": 0.0003,
    "tensorboard_log": "runs/HalfCheetah-v3__tqc__308614917__1676039138/HalfCheetah-v3",
    "lr_schedule": {
        ":type:": "<class 'function'>",
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    },
    "_last_obs": null,
    "_last_episode_starts": {
        ":type:": "<class 'numpy.ndarray'>",
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    },
    "_last_original_obs": {
        ":type:": "<class 'numpy.ndarray'>",
        ":serialized:": "gAWV/QAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaIAAAAAAAAAJzB6vVVlbS/cojkhHW/vz/WaZNyWo7wP+hlEycXd9I/As9c9bTw4j/rjK5wvGPwv4dtWDw/gNi/YNGzgEJy07+yIMJigDwpQBDMA3ik4Zs/MfUMt+7v8T+gz6EL3NzNP2jLV3JjA86/lljhrjkCCsAYJ0Vs6HL8P1XbEV28+PY/xe4yQxrZKMCUjAVudW1weZSMBWR0eXBllJOUjAJmOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRiSwFLEYaUjAFDlHSUUpQu"
    },
    "_episode_num": 1000,
    "use_sde": false,
    "sde_sample_freq": -1,
    "_current_progress_remaining": 0.0,
    "ep_info_buffer": {
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    "buffer_size": 1,
    "batch_size": 256,
    "learning_starts": 10000,
    "tau": 0.005,
    "gamma": 0.99,
    "gradient_steps": 1,
    "optimize_memory_usage": false,
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        "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
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        "add": "<function ReplayBuffer.add at 0x7fd9ebdee670>",
        "sample": "<function ReplayBuffer.sample at 0x7fd9ebdee700>",
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        "__abstractmethods__": "frozenset()",
        "_abc_impl": "<_abc._abc_data object at 0x7fd9ebde8500>"
    },
    "replay_buffer_kwargs": {},
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    "use_sde_at_warmup": false,
    "target_entropy": -6.0,
    "ent_coef": "auto",
    "target_update_interval": 1,
    "top_quantiles_to_drop_per_net": 2,
    "batch_norm_stats": [],
    "batch_norm_stats_target": []
}