Human-Feedback-Guided Multi-Agent Inverse Reinforcement Learning for Interactive Driving Behavior Modeling

This paper develops a meta-multi-agent reinforcement learning (meta-MARL) framework to enable fast adaptation of interactive policies in a multi-agent system (MAS). Meta-reinforcement learning (meta-RL) enables agents to rapidly adapt to new tasks/environments using a bi-level optimization mechanism. However, existing meta-RL generally focuses on single-agent systems. Extending these frameworks and algorithms to multi-agent systems poses additional challenges, as tasks are characterized by not only the environment but also agents' strategic interactions. To address these challenges, we model multi-agent reinforcement learning (MARL) problems as Markov games (MGs) and develop a meta-MARL framework for rapid interactive policy adaptation across a distribution of MGs. A new concept, called meta-NE, is defined to describe the desired solution concept in a meta-MARL problem. Sufficient conditions for the equivalence between a meta-NE and a stationary point of the gradient-play-based meta-MARL algorithm are established. Our evaluation on autonomous-driving tasks demonstrates that the proposed meta-MARL method achieves faster adaptation than pretrained MARL baselines, validating the effectiveness of our framework.

Multi-Agent Self-Driving (MASD) systems provide an effective solution for coordinating autonomous vehicles to reduce congestion and enhance both safety and operational efficiency in future intelligent transportation systems. Multi-Agent Reinforcement Learning (MARL) has emerged as a promising approach for developing advanced end-to-end MASD systems. However, achieving efficient and safe collaboration in dynamic MASD systems remains a significant challenge in dense scenarios with complex agent interactions. To address this challenge, we propose a novel collaborative(CO-) interaction-aware(-IN) MARL framework, named COIN. Specifically, we develop a new counterfactual individual-global twin delayed deep deterministic policy gradient (CIG-TD3) algorithm, crafted in a "centralized training, decentralized execution" (CTDE) manner, which aims to jointly optimize the individual objectives (navigation) and the global objectives (collaboration) of agents. We further introduce a dual-level interaction-aware centralized critic architecture that captures both local pairwise interactions and global system-level dependencies, enabling more accurate global value estimation and improved credit assignment for collaborative policy learning. We conduct extensive simulation experiments in dense urban traffic environments, which demonstrate that COIN consistently outperforms other advanced baseline methods in both safety and efficiency across various system sizes. These results highlight its superiority in complex and dynamic MASD scenarios, as further validated through real-world robot demonstrations. Supplementary videos are available at https://marmotlab.github.io/COIN/

Multi-agent inverse reinforcement learning offers a powerful framework not only for imitation learning but also for understanding the neural computations supporting social interactions and decision-making. Inferring rewards from interacting agents is challenging because observed choices reflect both personal preferences and expectations about others. Existing methods often require additional environment interaction, repeated policy optimization as an inner loop, or restrictive assumptions about environment dynamics and action spaces, limiting their applicability to fixed behavioral datasets. We introduce Multi-Agent Unilateral Inverse Temporal Difference Learning (MA-UITD), an offline method for inferring explicit agent-specific rewards from joint demonstrations in continuous-state environments with either discrete or bounded continuous actions. Its core idea is to represent the long-term consequences of each agent's own actions through belief-conditioned successor features that average over other agents' behavior. Shared reward features and agent-specific preference vectors connect these unilateral values to the target reward function, while inverse temporal-difference consistency and noise-contrastive behavioral learning enable inference without known dynamics or an inner policy-optimization loop. Across simulated cooperative, competitive, and general-sum tasks, MA-UITD outperforms baselines in discrete-action settings and infers behaviorally compatible reward profiles in continuous-action settings. When applied to experimentally collected social-foraging trajectories, MA-UITD inferred distinct spatial and interaction-dependent reward structures for leaders and followers, providing a quantitative characterization of latent behavioral objectives that may be relevant for understanding the neural computations underlying social behavior.