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Video status: Related supplementary video included; the original lecture recording has not been verified. Source details
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.
On video, the Spring 2026 lecture recordings are public on the RAIL YouTube channel (a 27-video playlist). The official syllabus and course home page have not caught up: they still say recordings are on bCourses and link the Fall 2023 playlist. The Fall 2023 recordings on the resources page are historical substitutes, not recordings of the 2026 lectures. The 2026 slides, sections, and assignments stay canonical; match recordings by lecture number.
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
The recordings restore the instructor's spoken explanation, but an external learner still lacks Ed threads, Gradescope feedback, in-class interaction, and office hours, which live in bCourses and the enrolled-student systems. 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.”
Course video sources
This post is a course overview or resource map with no single corresponding lecture. Checked live on 2026-10-10: the instructor published the Spring 2026 lecture recordings on the RAIL YouTube channel (27 videos, public since 2026-08-15). The course syllabus still says recordings are on bCourses and the course site still links the Fall 2023 playlist. Pick recordings by lecture number from the playlist.
The overview embeds Lecture 1 as an introductory supplement (the opening lecture, 44 minutes; its content was checked against the transcript as the opening and course introduction):
Course and recording entries:
- Official course and recording entry
- CS 185/285: Deep Reinforcement Learning (Spring 2026) — official RAIL YouTube playlist (27 videos)
Checked: 2026-10-10.
Content check: verified against the video transcript (2026-10-10): the transcript obtained reaches only the middle of the lecture (up to the learning-and-search part of the Bitter Lesson discussion); it covers the opening robot-grasping example, course logistics (prerequisites, homework, exam, final project), what RL is, how it differs from supervised learning on the IID assumption, applications, and Sutton's The Bitter Lesson, matching its role as an introductory supplement for this overview.
Update Log
- 2026-10-10: Added explicit video status and checked recording sources and access notes.
- 2026-10-10: Fixed an outdated recording statement. Lecture recordings are public on the RAIL YouTube channel; what remains in bCourses is Ed, Gradescope, in-class interaction, and office hours. The tldr, description, and body were rewritten to match.
- 2026-10-10: Rechecked video status. Found the public Spring 2026 playlist on the RAIL channel and linked it; this post has no single lecture, so the status stays official entry only.
- 2026-10-10: The overview now embeds the Spring 2026 Lecture 1 recording and its status is a related supplementary video, matching the other course overviews.
- 2026-10-10: Checked the video content against its transcript. Read the Lecture 1 transcript, confirming it is the opening and course introduction, and updated the earlier "transcript not read" note.
References
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