“Job Hunt As a PhD in RL: How It Actually Happens § Reinforcement Learning Reflections”, Lambert2022

“Job Hunt as a PhD in RL: How it Actually Happens § Reinforcement learning reflections”⁠, Nato Lambert2022-07-05 (similar)

“Large-Scale Retrieval for Reinforcement Learning”, Humphreys Et Al 2022

“Large-Scale Retrieval for Reinforcement Learning”⁠, Peter C. Humphreys, Arthur Guez, Olivier Tieleman, Laurent Sifre, Théophane Weber, Timothy Lillicrap2022-06-10 (retrieval AI⁠, AlphaGo; similar; bibliography)

“Boosting Search Engines With Interactive Agents”, Ciaramita Et Al 2022

“Boosting Search Engines with Interactive Agents”⁠, Massimiliano Ciaramita, Leonard Adolphs, Michelle Chen Huebscher, Sascha Rothe, Christian Buck, Thomas Hofmann2022-06-04 (retrieval AI⁠, T5 Transformer⁠, RL exploration⁠, Decision Transformer; similar; bibliography)

“Stochastic MuZero: Planning in Stochastic Environments With a Learned Model”, Antonoglou Et Al 2022

“Stochastic MuZero: Planning in Stochastic Environments with a Learned Model”⁠, Ioannis Antonoglou, Julian Schrittwieser, Sherjil Ozair, Thomas K. Hubert, David Silver2022-03-15 (similar)

“Policy Improvement by Planning With Gumbel”, Danihelka Et Al 2022

“Policy improvement by planning with Gumbel”⁠, Ivo Danihelka, Arthur Guez, Julian Schrittwieser, David Silver2022-03-04 (RL exploration⁠, AlphaGo; similar; bibliography)

“MuZero With Self-competition for Rate Control in VP9 Video Compression”, Mandhane Et Al 2022

“MuZero with Self-competition for Rate Control in VP9 Video Compression”⁠, Amol Mandhane, Anton Zhernov, Maribeth Rauh, Chenjie Gu, Miaosen Wang, Flora Xue, Wendy Shang, Derek Pang2022-02-14 (video analysis; similar)

“Procedural Generalization by Planning With Self-Supervised World Models”, Anand Et Al 2021

“Procedural Generalization by Planning with Self-Supervised World Models”⁠, Ankesh Anand, Jacob Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Théophane Weber2021-11-02 (RL exploration⁠, meta-learning⁠, RL scaling; similar; bibliography)

“Mastering Atari Games With Limited Data”, Ye Et Al 2021

“Mastering Atari Games with Limited Data”⁠, Weirui Ye, Shaohuai Liu, Thanard Kurutach, Pieter Abbeel, Yang Gao2021-10-30 (RL exploration; backlinks; similar)

“Proper Value Equivalence”, Grimm Et Al 2021

“Proper Value Equivalence”⁠, Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh2021-06-18 (end-to-end; similar; bibliography)

“Vector Quantized Models for Planning”, Ozair Et Al 2021

“Vector Quantized Models for Planning”⁠, Sherjil Ozair, Yazhe Li, Ali Razavi, Ioannis Antonoglou, Aäron van den Oord, Oriol Vinyals2021-06-08 (autoencoder NN⁠, video generation⁠, AI chess; similar)

“Learning and Planning in Complex Action Spaces”, Hubert Et Al 2021

“Learning and Planning in Complex Action Spaces”⁠, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Mohammadamin Barekatain, Simon Schmitt, David Silver2021-04-13 (similar)

“MuZero Unplugged: Online and Offline Reinforcement Learning by Planning With a Learned Model”, Schrittwieser Et Al 2021

“MuZero Unplugged: Online and Offline Reinforcement Learning by Planning with a Learned Model”⁠, Julian Schrittwieser, Thomas Hubert, Amol Mandhane, Mohammadamin Barekatain, Ioannis Antonoglou, David Silver2021-04-13 (RL scaling; similar; bibliography)

“Podracer Architectures for Scalable Reinforcement Learning”, Hessel Et Al 2021

“Podracer architectures for scalable Reinforcement Learning”⁠, Matteo Hessel, Manuel Kroiss, Aidan Clark, Iurii Kemaev, John Quan, Thomas Keck, Fabio Viola, Hado van Hasselt2021-04-13 (AI scaling/hardware⁠, meta-learning⁠, model-free RL⁠, RL scaling; similar; bibliography)

“Muesli: Combining Improvements in Policy Optimization”, Hessel Et Al 2021

“Muesli: Combining Improvements in Policy Optimization”⁠, Matteo Hessel, Ivo Danihelka, Fabio Viola, Arthur Guez, Simon Schmitt, Laurent Sifre, Theophane Weber2021-04-13 (model-free RL; backlinks; similar)

“Scaling Scaling Laws With Board Games”, Jones2021

“Scaling Scaling Laws with Board Games”⁠, Andy L. Jones2021-04-07 (CS/hardware⁠, AlphaGo⁠, RL scaling; backlinks; similar)

“Playing Nondeterministic Games through Planning With a Learned Model”, Willkens & Pollack2021

“Playing Nondeterministic Games through Planning with a Learned Model”⁠, Thomas Willkens, Jordan Pollack2021-03-05 (backlinks; similar)

“Visualizing MuZero Models”, Vries Et Al 2021

“Visualizing MuZero Models”⁠, Joery A. de Vries, Ken S. Voskuil, Thomas M. Moerland, Aske Plaat2021-02-25 (data visualization; similar; bibliography)

“Combining Off and On-Policy Training in Model-Based Reinforcement Learning”, Borges & Oliveira2021

“Combining Off and On-Policy Training in Model-Based Reinforcement Learning”⁠, Alexandre Borges, Arlindo Oliveira2021-02-24 (similar)

“Improving Model-Based Reinforcement Learning With Internal State Representations through Self-Supervision”, Scholz Et Al 2021

“Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision”⁠, Julien Scholz, Cornelius Weber, Muhammad Burhan Hafez, Stefan Wermter2021-02-10 (similar)

“On the Role of Planning in Model-based Deep Reinforcement Learning”, Hamrick Et Al 2020

“On the role of planning in model-based deep reinforcement learning”⁠, Jessica B. Hamrick, Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony2020-11-08 (similar)

“The Value Equivalence Principle for Model-Based Reinforcement Learning”, Grimm Et Al 2020

“The Value Equivalence Principle for Model-Based Reinforcement Learning”⁠, Christopher Grimm, André Barreto, Satinder Singh, David Silver2020-11-06 (end-to-end; similar; bibliography)

“Measuring Progress in Deep Reinforcement Learning Sample Efficiency”, Anonymous2020

“Measuring Progress in Deep Reinforcement Learning Sample Efficiency”⁠, Anonymous2020-09-28 (experience curves⁠, RL scaling; similar)

“Monte-Carlo Tree Search As Regularized Policy Optimization”, Grill Et Al 2020

“Monte-Carlo Tree Search as Regularized Policy Optimization”⁠, Jean-Bastien Grill, Florent Altché, Yunhao Tang, Thomas Hubert, Michal Valko, Ioannis Antonoglou, Rémi Munos2020-07-24 (AlphaGo; similar)

“Continuous Control for Searching and Planning With a Learned Model”, Yang Et Al 2020

“Continuous Control for Searching and Planning with a Learned Model”⁠, Xuxi Yang, Werner Duvaud, Peng Wei2020-06-12 (similar; bibliography)

“Agent57: Outperforming the Human Atari Benchmark”, Puigdomènech Et Al 2020

“Agent57: Outperforming the human Atari benchmark”⁠, Adrià Puigdomènech, Bilal Piot, Steven Kapturowski, Pablo Sprechmann, Alex Vitvitskyi, Daniel Guo, Charles Blundell2020-03-31 (RNN⁠, RL exploration⁠, RL scaling; backlinks; similar; bibliography)

“MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser Et Al 2019

“MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model”⁠, Julian Schrittwieser, Ioannis Antonoglou, Thomas Hubert, Karen Simonyan, Laurent Sifre, Simon Schmitt2019-11-19 (RNN⁠, AI chess⁠, AlphaGo; similar)

“Surprising Negative Results for Generative Adversarial Tree Search”, Azizzadenesheli Et Al 2018

“Surprising Negative Results for Generative Adversarial Tree Search”⁠, Kamyar Azizzadenesheli, Brandon Yang, Weitang Liu, Zachary C. Lipton, Animashree Anandkumar2018-06-15 (AlphaGo; similar)

“TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning”, Farquhar Et Al 2017

“TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning”⁠, Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson2017-10-31 (backlinks; similar; bibliography)

Wikipedia

1. Monte Carlo tree search

2. MuZero

3. Tensor processing unit