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Most exploration research on reinforcement learning (RL) has paid attentionto `the way of exploration', which is `how to explore'. The other explorationresearch, `when to explore', has not been the main focus of RL explorationresearch. The issue of `when' of a monolithic exploration in the usual RLexploration behaviour binds an exploratory action to an exploitational actionof an agent. Recently, a non-monolithic exploration research has emerged toexamine the mode-switching exploration behaviour of humans and animals. Theultimate purpose of our research is to enable an agent to decide when toexplore or exploit autonomously. We describe the initial research of anautonomous multi-mode exploration of non-monolithic behaviour in an optionsframework. The higher performance of our method is shown against the existingnon-monolithic exploration method through comparative experimental results.

An Autonomous Non-monolithic Agent with Multi-mode Exploration based on Options Framework