Causal Knowledge Transfer for Multi-Agent Reinforcement Learning in Dynamic Environments

2025-07-18

AI Summary

Multi-agent reinforcement learning (MARL) suffers from poor generalization and costly retraining when transferring knowledge across agents in non-stationary environments. Method: This paper proposes the first causal representation-based cross-agent knowledge transfer framework for MARL. It formulates policy recovery as a causal intervention, integrating macro-action sequence modeling with a local context-triggered lookup mechanism to enable decentralized, zero-shot online policy transfer. Contribution/Results: By incorporating causal reasoning, the approach enhances representation invariance, enabling agents to adapt to dynamic goals and environmental changes without retraining. Experiments in heterogeneous target scenarios demonstrate that the method bridges approximately 50% of the performance gap between random exploration and full retraining, significantly improving generalization capability and adaptation efficiency in non-stationary settings.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Causal Learning
• Knowledge Representation and Reasoning: Action, Change, and Causality

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

[Context] Multi-agent reinforcement learning (MARL) has achieved notable success in environments where agents must learn coordinated behaviors. However, transferring knowledge across agents remains challenging in non-stationary environments with changing goals. [Problem] Traditional knowledge transfer methods in MARL struggle to generalize, and agents often require costly retraining to adapt. [Approach] This paper introduces a causal knowledge transfer framework that enables RL agents to learn and share compact causal representations of paths within a non-stationary environment. As the environment changes (new obstacles), agents' collisions require adaptive recovery strategies. We model each collision as a causal intervention instantiated as a sequence of recovery actions (a macro) whose effect corresponds to a causal knowledge of how to circumvent the obstacle while increasing the chances of achieving the agent's goal (maximizing cumulative reward). This recovery action macro is transferred online from a second agent and is applied in a zero-shot fashion, i.e., without retraining, just by querying a lookup model with local context information (collisions). [Results] Our findings reveal two key insights: (1) agents with heterogeneous goals were able to bridge about half of the gap between random exploration and a fully retrained policy when adapting to new environments, and (2) the impact of causal knowledge transfer depends on the interplay between environment complexity and agents' heterogeneous goals.

Problem

• Transferring knowledge across agents in non-stationary MARL environments
• Generalizing causal representations for adaptive recovery strategies
• Enabling zero-shot adaptation without costly retraining

Innovation

• Causal knowledge transfer for MARL adaptation
• Zero-shot recovery action macros transfer
• Compact causal representations for dynamic environments