Multi-Agent Reinforcement Learning Guided by Signal Temporal Logic Specifications

TL;DR

This paper introduces a novel multi-agent reinforcement learning framework guided by Signal Temporal Logic (STL) specifications, improving reward design and safety in complex multi-agent systems.

Contribution

It presents an STL-guided reward framework for multi-agent reinforcement learning, integrating task and safety specifications to enhance performance and safety.

Findings

Significant reward performance improvements over non-STL methods

Marked increase in safety rates of multi-agent systems

Effective handling of complex interactions and heterogeneous goals

Abstract

Reward design is a key component of deep reinforcement learning, yet some tasks and designer's objectives may be unnatural to define as a scalar cost function. Among the various techniques, formal methods integrated with DRL have garnered considerable attention due to their expressiveness and flexibility to define the reward and requirements for different states and actions of the agent. However, how to leverage Signal Temporal Logic (STL) to guide multi-agent reinforcement learning reward design remains unexplored. Complex interactions, heterogeneous goals and critical safety requirements in multi-agent systems make this problem even more challenging. In this paper, we propose a novel STL-guided multi-agent reinforcement learning framework. The STL requirements are designed to include both task specifications according to the objective of each agent and safety specifications, and the robustness values of the STL specifications are leveraged to generate rewards. We validate the advantages of our method through empirical studies. The experimental results demonstrate significant reward performance improvements compared to MARL without STL guidance, along with a remarkable increase in the overall safety rate of the multi-agent systems.