An analytic solution to discrete Bayesian reinforcement learning
ACM Transactions on Multimedia Computing, Communications, and Applications, 2006, Pages 697-704.
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms come short of achieving this goal because the amount of exploration required is often too costly and/or too time consuming for online learning. As a result, RL is ...More
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