Expectation maximisation methods for solving (PO)MDPs and optimal control problems
Bayesian Time Series Models（2011）
摘要
As this book demonstrates, the development of efficient probabilistic inference techniques has made considerable progress in recent years, in particular with respect to exploiting the structure (e.g., factored, hierarchical or relational) of discrete and continuous problem domains. In this chapter we show that these techniques can be used also for solving Markov decision processes (MDPs) or partially observable MDPs (POMDPs) when formulated in terms of a structured dynamic Bayesian network (DBN).
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关键词
q function,gradient ascent,bellman equation,influence diagrams,reinforcement learning,maximum likelihood,unscented transform,computational statistics,markov decision process,free energy,independent component analysis,maximum a posteriori,planning,loopy belief propagation,value iteration,baum welch algorithm,hidden markov model,kullback leibler divergence
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