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pro vyhledávání: '"Umili, Elena"'
Non-markovian Reinforcement Learning (RL) tasks are very hard to solve, because agents must consider the entire history of state-action pairs to act rationally in the environment. Most works use symbolic formalisms (as Linear Temporal Logic or automa
Externí odkaz:
http://arxiv.org/abs/2408.08677
Autor:
Umili, Elena, Capobianco, Roberto
In this work, we introduce DeepDFA, a novel approach to identifying Deterministic Finite Automata (DFAs) from traces, harnessing a differentiable yet discrete model. Inspired by both the probabilistic relaxation of DFAs and Recurrent Neural Networks
Externí odkaz:
http://arxiv.org/abs/2408.08622