A New Framework for Multi-Agent Reinforcement Learning -- Centralized Training and Exploration with Decentralized Execution via Policy Distillation

Understand

Deep reinforcement learning (DRL) is a booming area of artificial intelligence.

• Many practical applications of DRL naturally involve more than one collaborative learners, making it important to study DRL in a multi-agent context.
• Previous research showed that effective learning in complex multi-agent systems demands for highly coordinated environment exploration among all the participating agents.
• Many researchers attempted to cope with this challenge through learning centralized value functions.