How can HRL improve large-scale robot task planning?
Scalable Hierarchical Reinforcement Learning for Hyper Scale Multi-Robot Task Planning
December 30, 2024
https://arxiv.org/pdf/2412.19538This paper tackles the problem of efficiently coordinating many robots in a massive warehouse (hyper-scale multi-robot task planning or MRTP) using hierarchical reinforcement learning (HRL). The robots must retrieve items, bring them to picking stations, and then store them efficiently, minimizing the total time taken (makespan).
Relevant to LLM-based multi-agent systems, the paper introduces:
- C2AMRTG (Asynchronous Multi-Robot Temporal Graph with Cycle Constraints): A graph representation for modeling the warehouse layout and robot tasks with asynchronous communication and constraints, similar to how LLMs could be used for structured knowledge representation in a multi-agent environment.
- Hierarchical Temporal Attention Network (HTAN): Uses attention mechanisms (common in LLMs) to handle variable-length inputs and outputs, addressing the scalability challenge of coordinating numerous agents.
- HCR-REINFORCE (HRL with Counterfactual Rollout Baseline): A training algorithm that tackles the credit assignment problem in HRL, analogous to disentangling individual contributions in multi-agent LLM scenarios where shared rewards are used.
- HCR2C (Multi-Stage Curriculum Learning): A training method that improves the system's ability to generalize to different warehouse layouts and scales (number of robots, items), similar to curriculum learning approaches used for fine-tuning LLMs on complex tasks.