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Multi-Agent Reinforcement Learning with Temporal Logic Specifications
[article]
2021
arXiv
pre-print
In 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. From a learning perspective these specifications provide a rich formal language with which to capture tasks or objectives, while from a logic and automated verification perspective the introduction of learning capabilities allows for practical applications in large, stochastic, unknown environments. The existing
arXiv:2102.00582v2
fatcat:wq2vons5sbhkbjjvq3iykksocy