Multi-Agent Reinforcement Learning: A Report on Challenges and Approaches

Understand

Reinforcement Learning (RL) is a learning paradigm concerned with learning to control a system so as to maximize an objective over the long term.

• This approach to learning has received immense interest in recent times and success manifests itself in the form of human-level performance on games like \textit{Go}.
• While RL is emerging as a practical component in real-life systems, most successes have been in Single Agent domains.
• This report will instead specifically focus on challenges that are unique to Multi-Agent Systems interacting in mixed cooperative and competitive environments.