The value-based reinforcement learning methods are known to overestimate action values such as deep Q-learning, which could lead to suboptimal policies. This problem also persists in an actor-critic algorithm. In this paper, we propose a novel mechanism to minimize its effects on both the critic and the actor. Our mechanism builds on Double Q-learning, by mixing update action value based on the minimum and maximum between a pair of critics to limit the overestimation. We then propose a specific adaptation to the Twin Delayed Deep Deterministic policy gradient algorithm (TD3) and show that the resulting algorithm not only reduces the observed overestimations, as hypothesized, but that this also leads to much better performance on several tasks.