Exploration strategy is an essential part of learning agents in model-based Reinforcement Learning. R-MAX and V-MAX are PAC-MDP strategies proved to have polynomial sample complexity; yet, their exploration behavior tend to be overly cautious in practice. We propose the principle of Increasingly Cautious Optimism (ICO) to automatically cut off unnecessarily cautious exploration, and apply ICO to R-MAX and V-MAX, yielding two new strategies, namely Increasingly Cautious R-MAX (ICR) and Increasingly Cautious V-MAX (ICV). We prove that both ICR and ICV are PAC-MDP, and show that their improvement is guaranteed by a tighter sample complexity upper bound. Then, we demonstrate their significantly improved performance through empirical results.
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