Description
This graduate seminar-style course explores many research topics in reinforcement learning (RL) and adaptive agent design. Students will engage with key papers in modern RL and related areas in adaptive agent design. The topics are Imitation Learning, Advances Policy Search, Empirical Evaluation in RL, Hierarchical RL, Advances in Exploration-Exploitation, Batch RL, Distributional RL, Multi-Agent RL, Safety and Robustness in RL, RL for Large Language Models, RL Acceleration, and Evolutionary Approach to Agent Design. The course emphasizes both algorithmic advances and theoretical foundations.