7/15/2023 0 Comments Vtrace options![]() MARWIL is reduced to behavior cloning (imitation learning) Parametersīeta – Scaling of advantages in exponential terms. ![]() fit () training ( *, beta: Optional =, bc_logstd_coeff: Optional =, moving_average_sqd_adv_norm_update_rate: Optional =, moving_average_sqd_adv_norm_start: Optional =, use_gae: Optional =, vf_coeff: Optional =, grad_clip: Optional =, **kwargs ) → .marwil.MARWILConfig # environment ( env = "CartPole-v1" ) > # Use to_dict() to get the old-style python config dict > # when running with tune. ) > # Set the config object's env, used for evaluation. input_ = "./rllib/tests/data/cartpole/large.json". > # Run this from the ray directory root. > from import BCConfig > from ray import tune > config = BCConfig () > # Print out some default values. BCConfig ( algo_class = None ) #ĭefines a configuration class from which a new BC Trainer can be built This makesīC try to match the behavior policy, which generated the offline data, disregarding any resulting rewards.īC requires the offline datasets API to be used.īC-specific configs (see also common configs): class .bc. With the only difference being the beta parameter force-set to 0.0. Our behavioral cloning implementation is directly derived from our MARWIL implementation, Offline # Behavior Cloning (BC derived from MARWIL implementation) # +RNN, +LSTM auto-wrapping, +Attention, +autoregĮxploration-based plug-ins (can be combined with any algo) RE3 (Random Encoders for Efficient Exploration)Ĭheck out the environments page to learn more about different environment types. MultiAgent LeelaChessZero (LeelaChessZero)Ĭuriosity (ICM: Intrinsic Curiosity Module) Multi-Agent Deep Deterministic Policy Gradient (MADDPG) QMIX Monotonic Value Factorisation (QMIX, VDN, IQN) ![]() Linear Upper Confidence Bound (BanditLinUCB) Model-Based Meta-Policy-Optimization (MB-MPO) Importance Weighted Actor-Learner Architecture (IMPALA)Īsynchronous Advantage Actor-Critic (A3C)ĭistributed Prioritized Experience Replay (Ape-X)ĭeep Q Networks (DQN, Rainbow, Parametric DQN)ĭeep Deterministic Policy Gradients (DDPG) Monotonic Advantage Re-Weighted Imitation Learning (MARWIL)Īsynchronous Proximal Policy Optimization (APPO)ĭecentralized Distributed Proximal Policy Optimization (DD-PPO) Behavior Cloning (BC derived from MARWIL implementation)
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