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Bridging the gap between reinforcement learning and control theory could unlock new synergies in optimizing unknown dynamical systems.
Retaining past knowledge can actually impede real-time adaptation in dynamic environments, leading to a new framework for optimizing continual learning.
Q-learning can provably converge to optimal policies in non-Markovian environments, even with hard state aggregation, using a simple action-commitment strategy.
