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Deep reinforcement learning methods exhibit impressive performance on a rangeof tasks but still struggle on hard exploration tasks in large environmentswith sparse rewards. To address this, intrinsic rewards can be generated usingforward model prediction errors that decrease as the environment becomes known,and incentivize an agent to explore novel states. While prediction-basedintrinsic rewards can help agents solve hard exploration tasks, they can sufferfrom catastrophic forgetting and actually increase at visited states. We firstexamine the conditions and causes of catastrophic forgetting in grid worldenvironments. We then propose a new method FARCuriosity, inspired by how humansand animals learn. The method depends on fragmentation and recall: an agentfragments an environment based on surprisal, and uses different local curiositymodules (prediction-based intrinsic reward functions) for each fragment so thatmodules are not trained on the entire environment. At each fragmentation event,the agent stores the current module in long-term memory (LTM) and eitherinitializes a new module or recalls a previously stored module based on itsmatch with the current state. With fragmentation and recall, FARCuriosityachieves less forgetting and better overall performance in games with variedand heterogeneous environments in the Atari benchmark suite of tasks. Thus,this work highlights the problem of catastrophic forgetting in prediction-basedcuriosity methods and proposes a solution.

Neuro-Inspired Fragmentation and Recall to Overcome Catastrophic Forgetting in Curiosity