# Deep Regret Actor-Critic

Multi-agent learning in continuous general-sum games has long struggled with non-stationarity, because agents maximizing their own payoffs make the learning target move. DRAC shifts the objective from payoff maximization to regret minimization, fixing opponents' actions and sampling unilateral deviations to build a stationary regret target, with a dual-head critic predicting best-response utility and regret separately. In a three-agent, five-round sequential Cournot oligopoly and a two-agent, five-stage proportional prize contest, DRAC's exploitability converged to 1.37×10⁻⁴ and 7.45×10⁻⁴, with mean relative absolute deviations of 0.011 and 0.030, both below MADDPG at the same training budget.

Multi-agent learning in continuous general-sum games has long struggled with non-stationarity, because agents maximizing their own payoffs make the learning target move. DRAC shifts the objective from payoff maximization to regret minimization, fixing opponents' actions and sampling unilateral deviations to build a stationary regret target, with a dual-head critic predicting best-response utility and regret separately. In a three-agent, five-round sequential Cournot oligopoly and a two-agent, five-stage proportional prize contest, DRAC's exploitability converged to 1.37×10⁻⁴ and 7.45×10⁻⁴, with mean relative absolute deviations of 0.011 and 0.030, both below MADDPG at the same training budget.