The Factored Policy Gradient planner (IPC-06 Version)

We present the Factored Policy Gradient (FPG) planner: a probabilistic temporal planner designed to scale to large planning domains by applying two significant approximations. Firstly, we use a “direct” policy search in the sense that we attempt to directly optimise a parameterised plan using gradient ascent. Secondly, the policy is factored into a per action mapping from a partial observation to the probabilility of executing, reflecting how desirable each action is. These two approximations — plus memory use that is independent of the number of states — allow us to scale to significantly larger planning domains than were previously feasible. Unlike other probabilistic temporal planners, FPG can attempt to optimise both makespan and the probability of reaching the goal. The version of FPG used in the IPC-06 competition optimises the makespan only, and turns off concurrent planning.