  Team competition in multi-agent Markov games is an increasingly important
setting for multi-agent reinforcement learning, due to its general
applicability in modeling many real-life situations. Multi-agent actor-critic
methods are the most suitable class of techniques for learning optimal policies
in the team competition setting, due to their flexibility in learning
agent-specific critic functions, which can also learn from other agents. In
many real-world team competitive scenarios, the roles of the agents naturally
emerge, in order to aid in coordination and collaboration within members of the
teams. However, existing methods for learning emergent roles rely heavily on
the Q-learning setup which does not allow learning of agent-specific
Q-functions. In this paper, we propose RAC, a novel technique for learning the
emergent roles of agents within a team that are diverse and dynamic. In the
proposed method, agents also benefit from predicting the roles of the agents in
the opponent team. RAC uses the actor-critic framework with role encoder and
opponent role predictors for learning an optimal policy. Experimentation using
2 games demonstrates that the policies learned by RAC achieve higher rewards
than those learned using state-of-the-art baselines. Moreover, experiments
suggest that the agents in a team learn diverse and opponent-aware policies.
