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We introduce an exploration bonus for deep reinforcement learning methodscalculated using self-organising feature maps. Our method uses adaptiveresonance theory (ART) providing online, unsupervised clustering to quantifythe novelty of a state. This heuristic is used to add an intrinsic reward tothe extrinsic reward signal for then to optimize the agent to maximize the sumof these two rewards. We find that this method was able to play the game Ordealat a human level after a comparable number of training epochs to ICMarXiv:1705.05464. Agents augmented with RND arXiv:1810.12894 were unable toachieve the same level of performance in our space of hyperparameters.

Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement Learning