<p>Lecture, three hours. Requisite: course 115. Recommended: course 119D. Introduction to fundamental principles of reinforcement learning theory, integrating biological and engineering perspectives. Review of brain systems that contribute to reinforcement learning and decision making. Introduction to common artificial intelligence algorithms for reinforcement learning. Comparative analysis of biological versus artificial reinforcement learning architectures. P/NP or letter grading.</p>

Psychology

College of Letters and Science

Container 1

PSYCH 119G - Reinforcement Learning in Biological and Artificial Agents

Reinforcement Learning in Biological and Artificial Agents

Gain practical experience implementing and experimenting with RL algorithms in Python using industry standard toolkits

Develop a deep understanding of reinforcement learning algorithms and their foundations

Critically analyze the strengths, limitations, and ethical implications of RL

Explore the parallels between RL and learning processes in the brain


General Catalog

Reinforcement Learning in Biological and Artificial Agents

PSYCH 119G

4 units