  MinMaxMin $Q$-learning is a novel optimistic Actor-Critic algorithm that
addresses the problem of overestimation bias ($Q$-estimations are
overestimating the real $Q$-values) inherent in conservative RL algorithms. Its
core formula relies on the disagreement among $Q$-networks in the form of the
min-batch MaxMin $Q$-networks distance which is added to the $Q$-target and
used as the priority experience replay sampling-rule. We implement MinMaxMin on
top of TD3 and TD7, subjecting it to rigorous testing against state-of-the-art
continuous-space algorithms-DDPG, TD3, and TD7-across popular MuJoCo and Bullet
environments. The results show a consistent performance improvement of
MinMaxMin over DDPG, TD3, and TD7 across all tested tasks.
