Toward Risk-based Optimistic Exploration for Cooperative Multi-Agent Reinforcement Learning

Understand

The multi-agent setting is intricate and unpredictable since the behaviors of multiple agents influence one another.

• To address this environmental uncertainty, distributional reinforcement learning algorithms that incorporate uncertainty via distributional output have been integrated with multi-agent reinforcement learning (MARL) methods, achieving state-of-the-art performance.
• However, distributional MARL algorithms still rely on the traditional $\epsilon$-greedy, which does not take cooperative strategy into account.
• In this paper, we present a risk-based exploration that leads to collaboratively optimistic behavior by shifting the sampling region of distribution.