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Continuously learning new tasks using high-level ideas or knowledge is a keycapability of humans. In this paper, we propose Lifelong reinforcement learningwith Sequential linear temporal logic formulas and Reward Machines (LSRM),which enables an agent to leverage previously learned knowledge to fastenlearning of logically specified tasks. For the sake of more flexiblespecification of tasks, we first introduce Sequential Linear Temporal Logic(SLTL), which is a supplement to the existing Linear Temporal Logic (LTL)formal language. We then utilize Reward Machines (RM) to exploit structuralreward functions for tasks encoded with high-level events, and proposeautomatic extension of RM and efficient knowledge transfer over tasks forcontinuous learning in lifetime. Experimental results show that LSRMoutperforms the methods that learn the target tasks from scratch by takingadvantage of the task decomposition using SLTL and knowledge transfer over RMduring the lifelong learning process.

Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines