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The paper proposes an active inference controller for adaptive traffic signal control in noisy IoT environments, outperforming DQN in idle times and CO2 emissions under sensor occlusion and adverse weather conditions.
Proposes LILAC+, a framework for safe continual reinforcement learning under nonstationarity that uses three adaptive safety mechanisms: context-based safety constraints, adaptation-speed constraints, and budget-to-state safety enforcement. Evaluations in simulated driving environments show reduced safety violations under distribution shift while maintaining competitive performance.