Reinforcement Learning & Agents Center

The Reinforcement Learning & Agents Center teaches sequential decision-making theory and autonomous agent architecture spanning deep RL, model-based world models, multi-agent systems, and LLM-based agents. Students formulate scientific exploration, experiment design, and adaptive control problems as reinforcement learning problems and train agents to solve them, progressing from single-agent settings to multi-agent collaboration. Through this progression, graduates reach the point where they can design and implement autonomous systems that make their own decisions in uncertain, changing environments.