  Recent reinforcement learning studies extensively explore the interplay
between cooperative and competitive behaviour in mixed environments. Unlike
cooperative environments where agents strive towards a common goal, mixed
environments are notorious for the conflicts of selfish and social interests.
As a consequence, purely rational agents often struggle to achieve and maintain
cooperation. A prevalent approach to induce cooperative behaviour is to assign
additional rewards based on other agents' well-being. However, this approach
suffers from the issue of multi-agent credit assignment, which can hinder
performance. This issue is efficiently alleviated in cooperative setting with
such state-of-the-art algorithms as QMIX and COMA. Still, when applied to mixed
environments, these algorithms may result in unfair allocation of rewards. We
propose BAROCCO, an extension of these algorithms capable to balance individual
and social incentives. The mechanism behind BAROCCO is to train two distinct
but interwoven components that jointly affect each agent's decisions. Our
meta-algorithm is compatible with both Q-learning and Actor-Critic frameworks.
We experimentally confirm the advantages over the existing methods and explore
the behavioural aspects of BAROCCO in two mixed multi-agent setups.
