Coordination, Communication and Robustness in Multi-Agents: An Industrial Network Scenario Using Trust Region Policy Optimization

Numerous practical uses necessitate multi-agent systems, including managing traffic, assigning tasks, regulating ant colonies, and operating self-driving cars, and drones. These systems involve multiple agents working together, communicating and engaging with their surroundings to achieve the highest possible total numerical reward. Deep Reinforcement Learning (DRL) approaches are used to address these multi-agent applications. In many circumstances, the use of agents raise challenges to safety and robustness. To address these issues, we develop a DRL based system in which multiple agents in an industrial network scenario interact with the real-world environment and act collaboratively and cooperatively. In proposed model, several agents collaborate with one another to complete tasks and maintain a safe state. To take actions cooperatively and collaboratively of agents in accordance with the safety robustness of policies, we apply DRL algorithms such as proximal policy optimization (PPO) and Trust Region Policy Optimization (TRPO) algorithms and DRL approaches. We apply Curriculum Learning (CL) for their better performance and training. In this study, a reward structure is also proposed which help agents to maintain their safe state. Mean reward, policy loss, value loss, value estimate and safety robustness are analyzed as performance matrix in this study. The results shows that the policy adopted in the proposed model perform comparably better than the other policies.