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Multi-Agent Reinforcement Learning with Temporal Logic Specifications
Adaptive Agents and Multi-Agent Systems, 2021In this paper, we study the problem of learning to satisfy temporal logic specifications with a group of agents in an unknown environment, which may exhibit probabilistic behaviour.
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In this paper we present a novel formalism for de ning properties over linear execution traces, namely elective temporal logic (ETL). Di erently from several other temporal logics, ETL is not dedicated to a speci c time model, e.g. discrete time or real time.
COSTA, GABRIELE, I. Matteucci
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We introduce a temporal logic for the specification of real-time systems. Our logic, TPTL, employs a novel quantifier construct for referencing time: the freeze quantifier binds a variable to the time of the local temporal context. TPTL is both a natural language for specification and a suitable
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