Table of Contents
🌏 中文版
Berkeley CS185/285 Spring 2026 is Sergey Levine's deep reinforcement learning course. Its public page lists slides for 25 lectures, nine discussion units, five assignments, and two default final projects. The starter-code repository is public too. That makes it an A3 material-based course, but not a fully open course.
The missing layer is video. The syllabus places Spring 2026 recordings in the bCourses Media Gallery. The Fall 2023 recordings listed on the official resources page are historical substitutes, not recordings of the 2026 lectures.
This series therefore treats the 2026 slides, sections, and assignments as canonical.
The six-part route
| Part | Official material | Guiding question |
|---|---|---|
| 1 | Full map | What is public, missing, and prerequisite? |
| 2 | L1–4, Sections 1–2, HW1 | Why does imitation learning face distribution shift? |
| 3 | L5–10, Sections 3–5, HW2–3 | How do policy gradients, actor-critic, DQN, and SAC connect? |
| 4 | L11–18, Sections 6–9, HW4–5 | How do inference, LLM RL, model-based RL, and offline RL connect? |
| 5 | L19–25 | How do exploration, theory, multitask learning, and open problems reshape the map? |
| 6 | All homework and projects | What runs on CPU, and what needs a GPU budget? |
The prerequisite is more than Python
The official prerequisite is CS189 or equivalent machine-learning preparation, plus familiarity with RL, numerical optimization, and ML. Before starting, write down how states, actions, transitions, rewards, policies, and value functions relate without consulting notes. If that fails, cover the MDP material first.
Read the compute labels before committing
HW1 and HW2 are CPU-first. Most HW3 experiments can run locally, but the handout estimates roughly three GPU hours for each expensive MsPacman or HalfCheetah run. HW4 defaults to a Modal H100 and requires four runs. An HW5 run can take about six hours, before tuning across algorithms and tasks.
The course's compute support applies to enrolled students. A self-learner should run the smallest configuration locally, confirm the pipeline, and only then purchase cloud time.
What this guide will not reconstruct
Without bCourses, an external learner lacks the instructor's spoken explanation, corrections, Ed threads, Gradescope feedback, and office hours. These articles explain the structure connecting public slides and assignments. They do not invent lecture remarks or relabel Fall 2023 video as Spring 2026.
A better completion criterion is an artifact: one derivation note, one implementation that succeeds on a small environment, a result table across seeds, and a failure analysis. That is closer to the course's actual work than merely “watching 25 lectures.”
References
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