arXiv:1611.01224
Sample Efficient Actor-Critic with Experience Replay
원문 초록
This paper presents an actor-critic deep reinforcement learning agent with experience replay that is stable, sample efficient, and performs remarkably well on challenging environments, including the discrete 57-game Atari domain and several continuous control problems. To achieve this, the paper introduces several innovations, including truncated importance sampling with bias correction, stochastic dueling network architectures, and a new trust region policy optimization method.
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DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertainty
Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity
Deep Reinforcement Learning with Gradient Eligibility Traces
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across Domains
Supervised Fine Tuning on Curated Data is Reinforcement Learning (and can be improved)
rQdia: Regularizing Q-Value Distributions With Image Augmentation
토론
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