Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)
Alan Fern, Sungwook Yoon, Robert Givan
We explore approximate policy iteration, replacing the usual cost- function learning step with a learning step in policy space. We give policy-language biases that enable solution of very large relational Markov decision processes (MDPs) that no previous technique can solve. In particular, we induce high-quality domain-speciﬁc planners for clas- sical planning domains (both deterministic and stochastic variants) by solving such domains as extremely large MDPs.