Robot Representation and Reasoning with Knowledge from Reinforcement Learning

28 Sep 2018 Keting Lu Shiqi Zhang Peter Stone Xiaoping Chen

Reinforcement learning (RL) agents aim at learning by interacting with an environment, and are not designed for representing or reasoning with declarative knowledge. Knowledge representation and reasoning (KRR) paradigms are strong in declarative KRR tasks, but are ill-equipped to learn from such experiences... (read more)

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