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AI Research
arXiv CS.AI·Jun 11·5 min read
Robust Instruction Compliance in Cooperative Multi-Agent Reinforcement Learning
TL;DR
In cooperative multi-agent reinforcement learning (MARL), ensuring agents follow instructions can be challenging, leading to inconsistent behaviors. A new framework called Robust Instruction Compliance (RIC) was developed to enhance adherence to instructions among agents.
✦ Why It Matters
Engineers can implement RIC to improve instruction adherence in multi-agent systems, enhancing reliability and performance.
Key Takeaways
- Use MAVIC to improve instruction compliance in MARL systems.
- Ensure consistent value estimates during instruction interruptions.
- Implement the actor-critic framework for practical applications.
- Test MAVIC in complex cooperative environments for best results.
How It Works
MAVIC modifies the bootstrapping target in reinforcement learning, correcting value estimates at the boundaries of instruction changes. This allows agents to maintain a coherent understanding of their objectives, even when faced with conflicting instructions.
By adjusting the incoming instruction objective and restoring the continuation value, MAVIC ensures that agents can effectively switch between tasks without losing performance.