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AI Research
arXiv CS.AI·May 25·8 min read
ARMS: Automatic Reward Shaping for Sparse-Reward Multi-Agent Reinforcement Learning
TL;DR
Multi-agent reinforcement learning (MARL) struggles when environmental rewards are sparse, making it hard for multiple agents to learn simultaneously without destabilizing each other's strategies. ARMS (Automatic Reward-shaping in Multi-agent Systems) automatically generates dense reward signals from sparse rewards using trajectory ranking while preserving Nash equilibria—the stable strategic outcomes in game theory.
✦ Why It Matters
Engineers can apply ARMS to accelerate multi-agent learning in sparse-reward domains while maintaining strategic stability and equilibrium properties.
Key Takeaways
- Use ARMS to automatically shape rewards in MARL applications.
- Implement trajectory ranking to derive dense rewards from sparse signals.
- Ensure strategic interactions are preserved in multi-agent environments.
- Increase exploration to stabilize learning and mitigate oscillatory behavior.
How It Works
ARMS operates by learning dense reward signals from sparse environmental feedback through a process called trajectory ranking. It reformulates the reward shaping process to ensure that agents' best-response strategies remain intact, which is crucial for maintaining the strategic structure of the game.
This is achieved by using conditional best-response reasoning, allowing the framework to adaptively shape rewards while preserving Nash equilibria.