Dynamic Action Interpolation linearly blends expert and RL actions with a time-decaying weight and claims faster learning and higher final rewards, but the supporting theory is asserted rather than derived.
Behavior priors for efficient reinforcement learning
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Dynamic Action Interpolation: A Universal Approach for Accelerating Reinforcement Learning with Expert Guidance
Dynamic Action Interpolation linearly blends expert and RL actions with a time-decaying weight and claims faster learning and higher final rewards, but the supporting theory is asserted rather than derived.