Episodic Future Thinking Mechanism for Multi-agent Reinforcement Learning

Understanding cognitive processes in multi-agent interactions is a primary goal in cognitive science. It can guide the direction of artificial intelligence (AI) research toward social decision-making in multi-agent systems, which includes uncertainty from character heterogeneity. In this paper, we introduce for a reinforcement learning (RL) agent, inspired by the cognitive processes observed in animals. To enable future thinking functionality, we first develop a that captures diverse characters with an ensemble of heterogeneous policies. The of an agent is defined as a different weight combination on reward components, representing distinct behavioral preferences.