Cooperative Multi-Agent Systems Using Distributed Reinforcement Learning Techniques

Reinforcement Learning in Cooperative Multi–Agent Systems

Reinforcement Learning is used in cooperative multi–agent systems differently for various problems. We provide a review on learning algorithms used for repeated common–payoff games, and stochastic general– sum games. Then these learning algorithms is compared with another algorithm for the credit assignment problem that attempts to correctly assign agents the awards that they deserve.

Argumentation Accelerated Reinforcement Learning for Cooperative Multi-Agent Systems

Multi-Agent Learning is a complex problem, especially in real-time systems. We address this problem by introducing Argumentation Accelerated Reinforcement Learning (AARL), which provides a methodology for defining heuristics, represented by arguments, and incorporates these heuristics into Reinforcement Learning (RL) by using reward shaping. We define AARL via argumentation and prove that it ca...

Cooperative Multi-agent Control Using Deep Reinforcement Learning

This work considers the problem of learning cooperative policies in complex, partially observable domains without explicit communication. We extend three classes of single-agent deep reinforcement learning algorithms based on policy gradient, temporal-difference error, and actor-critic methods to cooperative multi-agent systems. We introduce a set of cooperative control tasks that includes task...

An Algorithm for Distributed Reinforcement Learning in Cooperative Multi-Agent Systems

Distributed Reinforcement Learning in Multi-Agent Decision Systems

Decision problems can be usually solved using systems that implement diierent paradigms. These systems may be integrated into a single distributed system, with the expectation of obtaining a group performance more satisfactory than individual performances. Such a distributed system is what we call a Multi Agent Decision System (MADES), a special kind of Multi Agent System, that integrates sever...