  Value-based methods of multi-agent reinforcement learning (MARL), especially
the value decomposition methods, have been demonstrated on a range of
challenging cooperative tasks. However, current methods pay little attention to
the interaction between agents, which is essential to teamwork in games or real
life. This limits the efficiency of value-based MARL algorithms in the two
aspects: collaborative exploration and value function estimation. In this
paper, we propose a novel cooperative MARL algorithm named as interactive
actor-critic~(IAC), which models the interaction of agents from the
perspectives of policy and value function. On the policy side, a multi-agent
joint stochastic policy is introduced by adopting a collaborative exploration
module, which is trained by maximizing the entropy-regularized expected return.
On the value side, we use the shared attention mechanism to estimate the value
function of each agent, which takes the impact of the teammates into
consideration. At the implementation level, we extend the value decomposition
methods to continuous control tasks and evaluate IAC on benchmark tasks
including classic control and multi-agent particle environments. Experimental
results indicate that our method outperforms the state-of-the-art approaches
and achieves better performance in terms of cooperation.
