Reinforcement learning generalization using state aggregation with a maze solving problem
Reinforcement learning (RL) depends on constructing a lookup table for the value function of state action pairs. Consequently, when learning in environments with large scale state action space, RL fails to achieve practical convergence rates. Therefore, the need for generalizing the original state action space into more compact representation is crucial for many practical applications. In this paper, we propose a generalization technique using `state aggregation' We apply this generalization technique to Q learning, and show how to aggregate similar states together. The modified RL system architecture is presented along with the new algorithm. The proposed approach is tested and analyzed on a maze problem.
2012 Japan Egypt Conference on Electronics, Communications and Computers