Using Learned PSR Model for Planning under Uncertainty
- 信息技术－已发表论文 
As an alternative to partially observable Markov decision processes (POMDPs), Predictive State Representations (PSRs) is a recently developed method to model controlled dynamical systems. While POMDPs and PSRs provide general frameworks for solving the problem of planning under uncertainty, they rely crucially on having a known and accurate model of the environment. However, in real-world applications it can be extremely difficult to build an accurate model. In this paper, we use learned PSR model for planning under uncertainty, where the PSR model is learned from samples and may be inaccurate. We demonstrate the effectiveness of our algorithm on a standard set of POMDP test problems. Empirical results show that the algorithm we proposed is effective.