HyperMARL: Adaptive Hypernetworks for Multi-Agent RL

Kale-ab Tessera, Arrasy Rahman, Amos Storkey, Stefano V. Albrecht

Keywords: Multi-Agent Reinforcement Learning, Parameter Sharing, Specialisation, Behavioural Diversity

TL;DR: We show that hypernetworks are effective at adaptively learning task-specific behaviours (e.g. specialised or homogenous) in MARL, without modifying the learning objective or requiring preset diversity levels.

Abstract: Adaptability to specialised or homogeneous behaviours is critical in cooperative multi-agent reinforcement learning (MARL). Parameter sharing (PS) techniques, common for efficient adaptation, often limit behavioural diversity due to cross-agent gradient interference, which we show can be exacerbated by the coupling of observations and agent IDs. Current remedies typically add complexity through altered objectives, manual preset diversity levels, or sequential updates. We ask: can shared policies adapt without these complexities? We propose **HyperMARL**, a PS approach using hypernetworks for dynamic agent-specific parameters, without altering the RL objective or requiring preset diversity levels. HyperMARL's explicit *decoupling* of observation- and agent-conditioned gradients empirically reduces policy gradient variance, facilitates shared-policy adaptation (including specialisation), and helps mitigate cross-agent interference. Across diverse MARL benchmarks (up to 20 agents), requiring homogeneous, heterogeneous, or mixed behaviours, HyperMARL achieves competitive performance against key baselines -- fully shared, non-parameter sharing, and three diversity-promoting methods -- while preserving behavioural diversity comparable to non-parameter sharing. These findings establish HyperMARL as a versatile approach for adaptive MARL.

Submission Number: 20