Abstract:Despite the fact that deep reinforcement learning (RL) has surpassed human-level performances in various tasks, it still has several fundamental challenges such as extensive data requirement and lack of interpretability. We investigate the RL problem with non-Markovian reward functions to address such challenges. We enable an RL agent to extract high-level knowledge in the form of finite reward automata, a type of Mealy machines that encode non-Markovian reward functions. The finite reward automata can be conv… Show more
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