<ul><li>Actor-critic methods are commonly used in online reinforcement learning for continuous action spaces.</li><li>Unlike algorithms for discrete actions, RL algorithms for continuous actions typically use the Bellman operator instead of the Bellman optimality operator to model Q-values.</li><li>Incorporating the Bellman optimality operator into actor-critic frameworks accelerates learning but may introduce overestimation bias.</li><li>A proposed annealing approach gradually transitions from the Bellman optimality operator to the Bellman operator to improve learning efficiency and mitigate bias, outperforming existing approaches in locomotion and manipulation tasks.</li></ul>

Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning

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