Multi-Agent Evolutionary Reinforcement Learning Based on Cooperative Games

Despite the significant advancements in single-agent evolutionary reinforcement learning, research exploring evolutionary reinforcement learning within multi-agent systems is still in its nascent stage. The integration of evolutionary algorithms (EA) and reinforcement learning (RL) has partially mitigated RL's reliance on the environment and provided it with an ample supply of data. Nonetheless, existing studies primarily focus on the indirect collaboration between RL and EA, which lacks sufficient exploration on the effective balance of individual and team rewards. To address this problem, this study introduces game theory to establish a dynamic cooperation framework between EA and RL, and proposes a multi-agent evolutionary reinforcement learning based on cooperative games. This framework facilitates more efficient direct collaboration between RL and EA, enhancing individual rewards while ensuring the attainment of team objectives. Initially, a cooperative policy is formed through a joint network to simplify the parameters of each agent to speed up the overall training process. Subsequently, RL and EA engage in cooperative games to determine whether RL jointly optimizes the same policy based on Pareto optimal results. Lastly, through double objectives optimization, a balance between the two types of rewards is achieved, with EA focusing on team rewards and RL focusing on individual rewards. Experimental results demonstrate that the proposed algorithm outperforms its single-algorithm counterparts in terms of competitiveness.