Macro-Action Based Multi-Agent Instruction Following through Value Cancellation

1. 1Implement a MARL framework using a library like PyTorch or TensorFlow to handle multi-agent interactions
2. 2Define macro-actions as high-level instructions that can be composed of multiple low-level actions
3. 3Use value cancellation to decouple value estimates across instruction contexts and avoid inconsistent values
4. 4Train agents using a combination of reinforcement learning and natural language processing techniques to adapt to external instructions
5. 5Evaluate the performance of the macro-action based approach in a simulated environment with interrupting instructions

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

💡 Value cancellation helps to avoid inconsistent values when instructions interrupt macro-actions in MARL

🤖 Improve MARL with macro-action based instruction following through value cancellation! 📚