A Multi-Agent Centralized Strategy Gradient Reinforcement Learning Algorithm Based on State Transition

The prevalent utilization of deterministic strategy algorithms in Multi-Agent Deep Reinforcement Learning (MADRL) for collaborative tasks has posed a significant challenge in achieving stable and high-performance cooperative behavior. Addressing the need for the balanced exploration and exploitation of multi-agent ant robots within a partially observable continuous action space, this study introduces a multi-agent centralized strategy gradient algorithm grounded in a local state transition mechanism. In order to solve this challenge, the algorithm learns local state and local state-action representation from local observations and action values, thereby establishing a "local state transition" mechanism autonomously. As the input of the actor network, the automatically extracted local observation representation reduces the input state dimension, enhances the local state features closely related to the local state transition, and promotes the agent to use the local state features that affect the next observation state. To mitigate non-stationarity and reliability assignment issues in multi-agent environments, a centralized critic network evaluates the current joint strategy. The proposed algorithm, NST-FACMAC, is evaluated alongside other multi-agent deterministic strategy algorithms in a continuous control simulation environment using a multi-agent ant robot. The experimental results indicate accelerated convergence and higher average reward values in cooperative multi-agent ant simulation environments. Notably, in four simulated environments named Ant-v2 (2 x 4), Ant-v2 (2 x 4d), Ant-v2 (4 x 2), and Manyant (2 x 3), the algorithm demonstrates performance improvements of approximately 1.9%, 4.8%, 11.9%, and 36.1%, respectively, compared to the best baseline algorithm. These findings underscore the algorithm's effectiveness in enhancing the stability of multi-agent ant robot control within dynamic environments.