Policy Search via Density Estimation

Part of Advances in Neural Information Processing Systems 12 (NIPS 1999)

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Andrew Ng, Ronald Parr, Daphne Koller


We propose a new approach to the problem of searching a space of stochastic controllers for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP). Following several other authors, our approach is based on searching in parameterized families of policies (for example, via gradient descent) to optimize solution qual(cid:173) ity. However, rather than trying to estimate the values and derivatives of a policy directly, we do so indirectly using estimates for the proba(cid:173) bility densities that the policy induces on states at the different points in time. This enables our algorithms to exploit the many techniques for efficient and robust approximate density propagation in stochastic sys(cid:173) tems. We show how our techniques can be applied both to deterministic propagation schemes (where the MDP's dynamics are given explicitly in compact form,) and to stochastic propagation schemes (where we have access only to a generative model, or simulator, of the MDP). We present empirical results for both of these variants on complex problems.