New RL Method Improves Strategic Dialogue Agents

This research addresses a significant limitation in current reinforcement learning (RL) approaches for strategic dialogue agents: the "static-counterpart mismatch." Traditional RL often trains an agent against a fixed opponent or simulator, which can lead the agent to exploit specific regularities of that counterpart rather than learning generalizable strategies. In interactive settings like strategic dialogue, where the environment (the other agent) adapts, this fixed-counterpart training hinders the development of robust policies. To overcome this, the authors propose Isolated Bilateral Reinforcement Learning (IB-RL). In this framework, two agents coevolve through joint interactions, but each agent optimizes its own reward function and updates its policy entirely independently. This strict per-agent isolation during training encourages the development of more generalized strategies rather than policies tailored to a specific, static opponent. Evaluations on complex strategic dialogue tasks, such as Vehicle TeleSales and Deal-or-NoDeal, demonstrate IB-RL's superior performance. The method significantly improves success rates and agreement against independent, held-out counterparts compared to unilateral RL baselines, proving its effectiveness in fostering policies that generalize more effectively to unseen interactive scenarios.