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Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL, USA
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Switching policies derived from stationary policies can achieve optimality in constrained sc-LTL planning, balancing objectives and safety like never before.
A new two-step method for model-free reinforcement learning guarantees optimal policy convergence for co-safe LTL objectives, overcoming significant challenges in traditional approaches.
