MOMA-AC: A preference-driven actor-critic framework for continuous multi-objective multi-agent reinforcement learning

• URL: http://arxiv.org/abs/2511.18181v1
• Date: Sat, 22 Nov 2025 20:24:51 GMT
• Title: MOMA-AC: A preference-driven actor-critic framework for continuous multi-objective multi-agent reinforcement learning
• Authors: Adam Callaghan, Karl Mason, Patrick Mannion,
• Abstract summary: This paper introduces the first dedicated inner-loop actor-critic framework for continuous state and action spaces.<n>We instantiate this framework with Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG)<n>The framework combines a multi-headed actor network, a centralised critic, and an objective preference-conditioning architecture.
• Score: 3.312665722657581
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper addresses a critical gap in Multi-Objective Multi-Agent Reinforcement Learning (MOMARL) by introducing the first dedicated inner-loop actor-critic framework for continuous state and action spaces: Multi-Objective Multi-Agent Actor-Critic (MOMA-AC). Building on single-objective, single-agent algorithms, we instantiate this framework with Twin Delayed Deep Deterministic Policy Gradient (TD3) and Deep Deterministic Policy Gradient (DDPG), yielding MOMA-TD3 and MOMA-DDPG. The framework combines a multi-headed actor network, a centralised critic, and an objective preference-conditioning architecture, enabling a single neural network to encode the Pareto front of optimal trade-off policies for all agents across conflicting objectives in a continuous MOMARL setting. We also outline a natural test suite for continuous MOMARL by combining a pre-existing multi-agent single-objective physics simulator with its multi-objective single-agent counterpart. Evaluating cooperative locomotion tasks in this suite, we show that our framework achieves statistically significant improvements in expected utility and hypervolume relative to outer-loop and independent training baselines, while demonstrating stable scalability as the number of agents increases. These results establish our framework as a foundational step towards robust, scalable multi-objective policy learning in continuous multi-agent domains.

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