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

• RL_Notes__final_.pdfjubayer-ibn-hamid.github.io
• RLAlgsInMDPs.pdfsites.ualberta.ca
• [1709.06560] Deep Reinforcement Learning that Mattersarxiv.org
• Key Papers in Deep RL - Spinning Up documentationspinningup.openai.com
• rltheorybook_ABJKS.pdfrltheorybook.github.io
• [2506.22401] Exploration from a Primal-Dual Lens: Value-Incentivized Actor-Critic Methods for Sample-Efficient Online RLarxiv.org
• Understanding Policy Gradients | John Lambertjohnwlambert.github.io
• [2507.13181] Spectral Bellman Method: Unifying Representation and Exploration in RLarxiv.org
• [2606.05555] Representation Learning Enables Scalable Multitask Deep Reinforcement Learningarxiv.org
• Deep Reinforcement Learning: Pong from Pixelskarpathy.github.io
• Practical Deep Learning for Coders - Practical Deep Learningcourse.fast.ai
• RLHF & Post-Training Course by Nathan Lambertrlhfbook.com