Stigmergic Independent Reinforcement Learning for Multi-Agent Collaboration

Abstract

With the rapid evolution of wireless mobile devices, there emerges an increased need to design effective collaboration mechanisms between intelligent agents, so as to gradually approach the final collective objective through continuously learning from the environment based on their individual observations. In this regard, independent reinforcement learning (IRL) is often deployed in multi-agent collaboration to alleviate the problem of a non-stationary learning environment. However, behavioral strategies of intelligent agents in IRL can only be formulated upon their local individual observations of the global environment, and appropriate communication mechanisms must be introduced to reduce their behavioral localities. In this paper, we address the problem of communication between intelligent agents in IRL by jointly adopting mechanisms with two different scales. For the large scale, we introduce the stigmergy mechanism as an indirect communication bridge between independent learning agents, and carefully design a mathematical method to indicate the impact of digital pheromone. For the small scale, we propose a conflict-avoidance mechanism between adjacent agents by implementing an additionally embedded neural network to provide more opportunities for participants with higher action priorities. In addition, we present a federal training method to effectively optimize the neural network of each agent in a decentralized manner. Finally, we establish a simulation scenario in which a number of mobile agents in a certain area move automatically to form a specified target shape. Extensive simulations demonstrate the effectiveness of our proposed method.

References in corpus (8)

• A Brief Survey of Deep Reinforcement Learning
• AntNet: Distributed Stigmergetic Control for Communications Networks
• Learning to Communicate with Deep Multi-Agent Reinforcement Learning
• Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes
• Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability
• Learning Policies with External Memory
• ACCNet: Actor-Coordinator-Critic Net for "Learning-to-Communicate" with Deep Multi-agent Reinforcement Learning
• Stigmergy-based modeling to discover urban activity patterns from positioning data