RLAlgsInMDPs.pdf

N/A

Algorithms for Reinforcement Learning Draft of the lecture published in the Synthesis Lectures on Artificial Intelligence and Machine Learning series by Morgan & Claypool Publishers Csaba Szepesvári June 9, 2009∗ Contents 1 Overview…

saved by

• Akira Yoshiyama

related reading

• rltheorybook_ABJKS.pdfrltheorybook.github.io
• RL_Notes__final_.pdfjubayer-ibn-hamid.github.io
• [2602.11399] Can We Really Learn One Representation to Optimize All Rewards?arxiv.org
• [2506.22401] Exploration from a Primal-Dual Lens: Value-Incentivized Actor-Critic Methods for Sample-Efficient Online RLarxiv.org
• [1710.10044] Distributional Reinforcement Learning with Quantile Regressionarxiv.org
• Understanding Policy Gradients | John Lambertjohnwlambert.github.io
• [2602.05999] On the Role of Computation in Reinforcement Learningarxiv.org
• 1011.0686arxiv.org
• [1709.06560] Deep Reinforcement Learning that Mattersarxiv.org
• [1707.06347] Proximal Policy Optimization Algorithmsarxiv.org
• A (Long) Peek into Reinforcement Learning | Lil'Loglilianweng.github.io
• Reinforcement Learning in Newcomblike Problemsproceedings.neurips.cc