AINeutralarXiv – CS AI · Mar 34/104
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Sample-efficient and Scalable Exploration in Continuous-Time RL
Researchers introduce COMBRL, a new reinforcement learning algorithm designed for continuous-time systems using nonlinear ordinary differential equations. The algorithm achieves sublinear regret and better sample efficiency compared to existing methods by combining probabilistic models with uncertainty-aware exploration.