Adapting reinforcement learning to deliberative policy negotiations

Ascertain the methodological adaptations required to apply common reinforcement learning algorithms, including Q-learning, to dynamic and complex multi-agent policy negotiations and deliberation procedures, and demonstrate their suitability in non-stationary social decision-making contexts.

Background

Reinforcement learning (RL) is proposed to enhance agent-based models by enabling adaptive, goal-driven behavior. However, applying RL to complex, non-stationary multi-agent social systems—such as deliberative democratic processes—poses significant methodological challenges.

The authors explicitly note uncertainty regarding how standard RL methods like Q-learning can be adapted to policy negotiation and deliberation settings characterized by dynamic, complex interactions.