Before revision:  We then augment the state representation of the puzzle with straightforward hint features that are extracted from the behavior of that agent. Finally, we train a forward looking agent with this informed augmented state. We demonstrate that this simple "access" to partial backward plansleads to a substantial performance boost. On the challenging domain of the Sokoban puzzle, our RL approach substantially surpasses the best learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted solution .
After revision:  We then augment the state representation of the puzzle with straightforward hint features . This allows the learned forward agent to leverage information from backward plans, without mimicking their policy. We demonstrate the efficacy of our approach on the challenging game of Sokoban, where we substantially surpass learned solvers that generalize over levels, and is competitive with SOTA performance of the best highly-crafted solution .