Adapting Double Q-Learning for Continuous Reinforcement Learning

Majority of off-policy reinforcement learning algorithms use overestimation bias control techniques. Most of these techniques rooted in heuristics, primarily addressing the consequences of overestimation rather than its fundamental origins. In this work we present a novel approach to the bias correction, similar in spirit to Double Q-Learning. We propose using a policy in form of a mixture with two components. Each policy component is maximized and assessed by separate networks, which removes any basis for the overestimation bias. Our approach shows promising near-SOTA results on a small set of MuJoCo environments.