Macro-Action Based Multi-Agent Instruction Following through Value Cancellation
📰 ArXiv cs.AI
Learn how to implement macro-action based multi-agent instruction following through value cancellation to improve MARL in real-world scenarios
Action Steps
- Implement a MARL framework using a library like PyTorch or TensorFlow to handle multi-agent interactions
- Define macro-actions as high-level instructions that can be composed of multiple low-level actions
- Use value cancellation to decouple value estimates across instruction contexts and avoid inconsistent values
- Train agents using a combination of reinforcement learning and natural language processing techniques to adapt to external instructions
- Evaluate the performance of the macro-action based approach in a simulated environment with interrupting instructions
Who Needs to Know This
Researchers and engineers working on multi-agent reinforcement learning (MARL) and natural language processing (NLP) can benefit from this approach to improve instruction following in complex environments
Key Insight
💡 Value cancellation helps to avoid inconsistent values when instructions interrupt macro-actions in MARL
Share This
🤖 Improve MARL with macro-action based instruction following through value cancellation! 📚
Key Takeaways
Learn how to implement macro-action based multi-agent instruction following through value cancellation to improve MARL in real-world scenarios
Full Article
Title: Macro-Action Based Multi-Agent Instruction Following through Value Cancellation
Abstract:
arXiv:2605.12655v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long-horizon objectives. However, conditioning rewards on instructions introduces a fundamental failure mode as Bellman updates couple value estimates across instruction contexts, leading to inconsistent values when instructions interrupt macro-actions. We propose Macro-Actio
Abstract:
arXiv:2605.12655v1 Announce Type: new Abstract: Multi-agent reinforcement learning (MARL) in real-world use cases may need to adapt to external natural language instructions that interrupt ongoing behavior and conflict with long-horizon objectives. However, conditioning rewards on instructions introduces a fundamental failure mode as Bellman updates couple value estimates across instruction contexts, leading to inconsistent values when instructions interrupt macro-actions. We propose Macro-Actio
DeepCamp AI