Reviews: Hybrid Reward Architecture for Reinforcement Learning

R5: Summary: This paper builds on the basic idea of the Horde architecture: learning many value functions in parallel with off-policy reinforcement learning. This paper shows that learning many value functions in parallel improves the performance on a single main task. The novelty here lies in a particular strategy for generating many different reward functions and how to combine them to generate behavior. The results show large improvements in performance in an illustrative grid world and Miss Pac-man. Decision: This paper is difficult to access.