TL;DR
This paper introduces a conflict-aware gradient adjustment method for multi-agent reinforcement learning that promotes cooperation and fairness among agents in mixed-motive environments, outperforming existing approaches.
Contribution
It presents a novel adaptive gradient adjustment technique that explicitly resolves conflicts between individual and collective objectives, ensuring fairness and improving social welfare.
Findings
Outperforms baselines in social welfare metrics
Guarantees monotonic improvement in fairness and collective goals
Demonstrates effectiveness in sequential social dilemma environments
Abstract
Multi-agent reinforcement learning in mixed-motive settings presents a fundamental challenge: agents must balance individual interests with collective goals, which are neither fully aligned nor strictly opposed. To address this, reward restructuring methods such as gifting and intrinsic motivation have been proposed. However, these approaches primarily focus on promoting cooperation by managing the trade-off between individual and collective returns, without explicitly addressing fairness with respect to the agents' task-specific rewards. In this paper, we propose an adaptive conflict-aware gradient adjustment method that promotes cooperation while ensuring fairness in individual rewards. The proposed method dynamically balances policy gradients derived from individual and collective objectives in situations where the two objectives are in conflict. By explicitly resolving such…
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