Deep RL Bootcamp - Lectures

Lectures

26-27 August 2017 | Berkeley CA Lectures Core Lecture 1 Intro to MDPs and Exact Solution Methods -- Pieter Abbeel (video | slides) Core Lecture 2 Sample-based Approximations and Fitted Learning -- Rocky Duan (video | slides) Core Lecture 3 DQN + Variants -- Vlad Mnih (video | slides) Core Lecture 4a Policy Gradients and Actor Critic -- Pieter Abbeel (video | slides) Core Lecture 4b Pong from Pixels -- Andrej Karpathy (video | slides) Core Lecture 5 Natural Policy Gradients, TRPO, and PPO -- John Schulman (video | slides) Core Lecture 6 Nuts and Bolts of Deep RL Experimentation --…

saved by

• Lydia Nottingham
• Megan Walker
• Akira Yoshiyama

related reading

• Key Papers in Deep RL - Spinning Up documentationspinningup.openai.com
• RL_Notes__final_.pdfjubayer-ibn-hamid.github.io
• Understanding Policy Gradients | John Lambertjohnwlambert.github.io
• [1709.06560] Deep Reinforcement Learning that Mattersarxiv.org
• RLAlgsInMDPs.pdfsites.ualberta.ca
• [2506.22401] Exploration from a Primal-Dual Lens: Value-Incentivized Actor-Critic Methods for Sample-Efficient Online RLarxiv.org
• Deep Reinforcement Learning: Pong from Pixelskarpathy.github.io
• Selected Publicationsjoschu.net
• [2602.11399] Can We Really Learn One Representation to Optimize All Rewards?arxiv.org
• State of RL for reasoning LLMs | A. Weersaweers.de
• Part 2: Kinds of RL Algorithms - Spinning Up documentationspinningup.openai.com
• A vision researcher’s guide to some RL stuff: PPO & GRPO - Yuge (Jimmy) Shiyugeten.github.io