The Parti-Game Algorithm for Variable Resolution Reinforcement Learning in Multidimensional State-Spaces

Parti-game is a new algorithm for learning from delayed rewards in high dimensional real-valued state-spaces. In high dimensions it is essential that learning does not explore or plan over state space uniformly. Part i-game maintains a decision-tree partitioning of state-space and applies game-theory and computational geom(cid:173) etry techniques to efficiently and reactively concentrate high reso(cid:173) lution only on critical areas. Many simulated problems have been tested, ranging from 2-dimensional to 9-dimensional state-spaces, including mazes, path planning, non-linear dynamics, and uncurl(cid:173) ing snake robots in restricted spaces. In all cases, a good solution is found in less than twenty trials and a few minutes.