Adaptive cooperation in multi-agent reinforcement learning (MARL) requires policies to express homogeneous, specialised, or mixed behaviours, yet achieving this adaptivity remains a critical challenge. While parameter sharing (PS) is standard for efficient learning, it notoriously suppresses the behavioural diversity required for specialisation. This failure is largely due to cross-agent gradient interference, a problem we find is surprisingly exacerbated by the common practice of coupling agent IDs with observations. Existing remedies typically add complexity through altered objectives, manual preset diversity levels, or sequential updates - raising a fundamental question: can shared policies adapt without these intricacies? We propose a solution built on a key insight: an agent-conditioned hypernetwork can generate agent-specific parameters and decouple observationand agent-conditioned gradients, directly countering the interference from coupling agent IDs with observations. Our resulting method, HyperMARL, avoids the complexities of prior work and empirically reduces policy gradient variance. Across diverse MARL benchmarks (22 scenarios, up to 30 agents), HyperMARL achieves performance competitive with six key baselines while preserving behavioural diversity comparable to non-parameter sharing methods, establishing it as a versatile and principled approach for adaptive MARL.

Adaptive cooperation in multi-agent reinforcement learning (MARL) requires policies to express homogeneous, specialised, or mixed behaviours, yet achieving this adaptivity remains a critical challenge. While parameter sharing (PS) is standard for efficient learning, it notoriously suppresses the behavioural diversity required for specialisation. This failure is largely due to cross-agent gradient interference, a problem we find is surprisingly exacerbated by the common practice of . Existing remedies typically add complexity through altered objectives, manual preset diversity levels, or sequential updates -- raising a fundamental question: We propose a solution built on a key insight: an agent-conditioned hypernetwork can generate agent-specific parameters and observation-and agent-conditioned gradients, directly countering the interference from coupling agent IDs with observations.

Balancing individual specialisation and shared behaviours is a critical challenge in multi-agent reinforcement learning (MARL). Existing methods typically focus on encouraging diversity or leveraging shared representations. Full parameter sharing (FuPS) improves sample efficiency but struggles to learn diverse behaviours when required, while no parameter sharing (NoPS) enables diversity but is computationally expensive and sample inefficient. To address these challenges, we introduce HyperMARL, a novel approach using hypernetworks to balance efficiency and specialisation. HyperMARL generates agent-specific actor and critic parameters, enabling agents to adaptively exhibit diverse or homogeneous behaviours as needed, without modifying the learning objective or requiring prior knowledge of the optimal diversity. Furthermore, HyperMARL decouples agent-specific and state-based gradients, which empirically correlates with reduced policy gradient variance, potentially offering insights into its ability to capture diverse behaviours. Across MARL benchmarks requiring homogeneous, heterogeneous, or mixed behaviours, HyperMARL consistently matches or outperforms FuPS, NoPS, and diversity-focused methods, achieving NoPS-level diversity with a shared architecture. These results highlight the potential of hypernetworks as a versatile approach to the trade-off between specialisation and shared behaviours in MARL.