Poster Abstract: Model-Free Reinforcement Learning for Symbolic Automata-encoded Objectives

We provide the first solution for model-free reinforcement learning of -regular objectives for Markov decision processes (MDPs). We present a constructive reduction from the almost-sure satisfaction of -regular objectives to an almost-sure ...

Recent success in reinforcement learning (RL) has brought renewed attention to the design of reward functions by which agent behavior is reinforced or deterred. Manually designing reward functions is tedious and error-prone. An alternative approach is ...

Limited automata are one-tape Turing machines which are allowed to rewrite each tape cell only in the first d visits, for a given constant d. For each d ≥ 2, these devices characterize the class of context-free languages. We investigate the equivalence ...