Program Overview

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Introduction to the Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley

The Department of Electrical Engineering and Computer Sciences (EECS) at UC Berkeley offers one of the strongest research and instructional programs in this field anywhere in the world.

Academics

Overview

The EECS department provides a range of academic programs, including undergraduate and graduate admissions and programs.

Undergraduate Admissions & Programs

• CS Major
• EECS Major
• EECS/CS Program Comparison Chart
• Second Bachelor's Degree
• Summer Research
• Cal Day

Graduate Admissions & Programs

• Grad Admissions FAQ
• Industry-Oriented Programs
• Research-Oriented Programs
• Fellowships
• Adding the EECS/CS M.S. From Another Department
• Recommended Coursework

Courses

• EE Courses
• CS Courses

Research

Overview

Research is the foundation of Berkeley EECS. Faculty, students, and staff work together on cutting-edge projects that cross disciplinary boundaries to improve everyday life and make a difference.

Areas

The department has various research areas, including:

• Centers & Labs
• Colloquium
• BEARS Symposium
• Technical Reports
• Ph.D. Dissertations

People

Overview

EECS faculty, students, staff, and alumni are central to our success as one of the most thriving and distinguished departments on the Berkeley campus.

Directory

• Leadership
• Faculty
• Staff
• Students
• Alumni

CS 285: Deep Reinforcement Learning, Decision Making, and Control

Catalog Description

Intersection of control, reinforcement learning, and deep learning. Deep learning methods, which train large parametric function approximators, achieve excellent results on problems that require reasoning about unstructured real-world situations.

Units

3

Student Learning Outcomes

• Provide students with foundational knowledge to understand deep reinforcement learning algorithms
• Provide an opportunity to embark on a research-level final project with support from course staff
• Provide hands-on experience with several commonly used RL algorithms
• Provide students with an overview of advanced deep reinforcement learning topics, including current research trends

Prerequisites

CS189/289A or equivalent is a prerequisite for the course. This course will assume some familiarity with reinforcement learning, numerical optimization, and machine learning, as well as a basic working knowledge of how to train deep neural networks.

Formats

• Spring: 3.0 hours of lecture per week
• Fall: 3.0 hours of lecture per week

Grading Basis

Letter

Final Exam Status

No final exam

Class Schedule (Spring 2026)

CS 185/285 – We 09:00-09:59, Hearst Field Annex A1; Fr 08:00-09:59, Hearst Field Annex A1 – Sergey Levine

Class Notes

• Lectures will be recorded.