AINeutralarXiv – CS AI · 7h ago6/10
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PAC-Bayesian Reinforcement Learning Trains Generalizable Policies
Researchers have developed a novel PAC-Bayesian generalization bound for reinforcement learning that addresses the sequential data dependencies problem, enabling non-vacuous generalization certificates for off-policy algorithms like Soft Actor-Critic. The work introduces PB-SAC, an algorithm that leverages this bound to guide exploration while maintaining competitive performance on continuous control tasks.