Towards a Second Generation RandomWalk Planner: an Experimental Exploration

Random walks have become a popular component of recent planning systems. The increased explo-ration is a valuable addition to more exploitative search methods such as Greedy Best First Search (GBFS). A number of successful planners which incorporate random walks have been built. The work presented here aims to exploit the experience gained from building those systems. It begins a systematic study of the design space and alterna-tive choices for building such a system, and de-velops a new random walk planner from scratch, with careful experiments along the way. Four ma-jor insights are: 1. a high state evaluation frequency is usually superior to the endpoint-only evaluation used in earlier systems, 2. adjusting the restarting parameter according to the progress speed in the search space performs better than any fixed setting, 3. biasing the action selection towards preferred operators of only the current state is better than Monte Carlo Helpful Actions, which depend on the number of times an action has been a preferred op-erator in previous walks, and 4. even simple forms of random walk planning can compete with GBFS.