Overview

Undergraduate research project at KAIST exploring how artificial agents can learn cognitive policies through competitive multi-agent reinforcement learning. This work was inspired by how humans develop cognitive abilities through social interaction and competition.

Key Achievements

  • Best Paper Award: 2016 Fall Korea Intelligent Information System Conference
  • Developed multi-agent RL framework for cognitive policy learning
  • Demonstrated emergence of complex behaviors through agent competition
  • Explored connections between computational models and cognitive science

Technical Approach

Investigated whether AI agents can develop sophisticated cognitive strategies by competing with each other, similar to how humans learn through social competition and cooperation. The framework allows agents to develop increasingly complex policies through iterative competitive interactions.

Tech Stack

  • Method: Multi-Agent Reinforcement Learning
  • Domain: Cognitive Science, Policy Learning
  • Institution: KAIST, Lab for Brain and Machine Intelligence

Period

March 2016 - February 2017

Impact

This was the first major research project as an undergraduate at KAIST, conducted as part of Individual Research at the Lab for Brain and Machine Intelligence. The insights from multi-agent learning influenced later research on sequential modeling, user behavior understanding, and brain-inspired AI approaches.

Publications

  • Jungbae Park, Juno Kim, Sang Wan Lee - “Multi-agent Cognitive Policy Learning: Reinforcement Learning Through Competition” (Korea Intelligent Information System Conference, Fall 2016) - Best Paper Award