About

Interactive Multi-Agent Reinforcement Learning is a year-long project taken up by a group of members at IEEE-NITK. In this project, we aim to develop and improve multi-agent reinforcement learning algorithms that enable interactive, collaborative, and negotiating behaviors in the mixed cooperative-competitive multi-player games (like Sequential Social Dilemmas, particle, and MAgent environments).

This blog will be used to share articles on various topics in single and multi-agent reinforcement learning.

Team Members:

• Videh Raj Nema
• Manav Singhal
• Shreya Namath
• Aryaman M
• Mudit Singhal