  In this work, we consider the problem of computing optimal actions for
Reinforcement Learning (RL) agents in a co-operative setting, where the
objective is to optimize a common goal. However, in many real-life
applications, in addition to optimizing the goal, the agents are required to
satisfy certain constraints specified on their actions. Under this setting, the
objective of the agents is to not only learn the actions that optimize the
common objective but also meet the specified constraints. In recent times, the
Actor-Critic algorithm with an attention mechanism has been successfully
applied to obtain optimal actions for RL agents in multi-agent environments. In
this work, we extend this algorithm to the constrained multi-agent RL setting.
The idea here is that optimizing the common goal and satisfying the constraints
may require different modes of attention. By incorporating different attention
modes, the agents can select useful information required for optimizing the
objective and satisfying the constraints separately, thereby yielding better
actions. Through experiments on benchmark multi-agent environments, we show the
effectiveness of our proposed algorithm.
