FF+FPG: guiding a policy-gradient planner
Olivier Buffet, Douglas Aberdeen · ANU Open Research (Australian National University) · 2007
The Factored Policy-Gradient planner (FPG) (Buffet & Aberdeen 2006) was a successful competitor in the probabilistic track of the 2006 International Planning Competition (IPC). FPG is innovative because it scales to large planning domains through the use of Reinforcement Learning. It essentially performs a stochastic local search in policy space. FPG's weakness is potentially long learning times, as it initially acts randomly and progressively improves its policy each time the goal is reached. This paper shows how to use an external teacher to guide FPG's exploration. While any teacher can be used, we concentrate on the actions suggested by FF's heuristic (Hoffmann 2001), as FF-replan has proved efficient for probabilistic re-planning. To achieve this, FPG must learn its own policy while following another. We thus extend FPG to off-policy learning using importance sampling (Glynn & Iglehart 1989; Peshkin & Shelton 2002). The resulting algorithm is presented and evaluated on IPC benchmarks.