Deriving Subgoals Autonomously to Accelerate Learning in Sparse Reward Domains.

Sparse reward games, such as the infamous Montezumas Revenge, pose a significant challenge for Reinforcement Learning (RL) agents. Hierarchical RL, which promotes efficient exploration via subgoals, has shown promise in these games. However, existing agents rely either on human domain knowledge or slow autonomous methods to derive suitable subgoals. In this work, we describe a new, autonomous approach for deriving subgoals from raw pixels that is more efficient than competing methods. We propose a novel intrinsic reward scheme for exploiting the derived subgoals, applying it to three Atari games with sparse rewards. Our agents performance is comparable to that of state-of-the-art methods, demonstrating the usefulness of the subgoals found