How to speed up multi-agent RL training?
Towards Better Sample Efficiency in Multi-Agent Reinforcement Learning via Exploration
This research explores ways to make multi-agent reinforcement learning (MARL) more efficient, specifically within the context of a simulated football environment. The core idea is to improve "sample efficiency"—how much training data is needed to achieve good performance—by enhancing exploration strategies. They modify the existing TiZero MARL algorithm, introducing a self-supervised intrinsic reward and a random network distillation bonus to encourage agents to explore more diverse actions and states. Results indicate that random network distillation significantly improves learning speed and leads to more offensive gameplay compared to the original TiZero.
Key points for LLM-based multi-agent systems:
- Exploration is crucial: Like LLMs, multi-agent systems benefit from robust exploration to avoid converging on suboptimal strategies. This research demonstrates the value of augmenting reward functions to guide exploration.
- Sample efficiency matters: Training complex multi-agent systems, especially with computationally intensive LLMs, can be very expensive. Improving sample efficiency, as shown here, is critical for practical applications.
- Emergent behavior: Modifying reward structures can shape the emergent behavior of multi-agent systems. In this case, changes led to more offensive play, highlighting how reward design can be used to steer the system towards desired outcomes.
- Adaptation to complex environments: The football environment serves as a challenging testbed for multi-agent coordination, similar to complex real-world scenarios where LLM-based agents might be deployed. The techniques explored here could potentially be adapted to those scenarios.