“‘MuZero’ Tag”,2019-12-27 (RNN, AI chess, hidden-information game, AlphaGo, RL scaling)⁠:

Bibliography for tag reinforcement-learning/model/muzero, most recent first: 2 related tags, 37 annotations, & 6 links (parent).

• See Also
• Links
  • “AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning”, Mathieu et al 2023
  • “Job Hunt As a PhD in RL: How It Actually Happens § Reinforcement Learning Reflections”, Lambert2022
  • “Large-Scale Retrieval for Reinforcement Learning”, Humphreys et al 2022
  • “Boosting Search Engines With Interactive Agents”, Ciaramita et al 2022
  • “Stochastic MuZero: Planning in Stochastic Environments With a Learned Model”, Antonoglou et al 2022
  • “Policy Improvement by Planning With Gumbel”, Danihelka et al 2022
  • “MuZero With Self-Competition for Rate Control in VP9 Video Compression”, Mandhane et al 2022
  • “Procedural Generalization by Planning With Self-Supervised World Models”, Anand et al 2021
  • “Mastering Atari Games With Limited Data”, Ye et al 2021
  • “Proper Value Equivalence”, Grimm et al 2021
  • “Vector Quantized Models for Planning”, Ozair et al 2021
  • “Muesli: Combining Improvements in Policy Optimization”, Hessel et al 2021
  • “Podracer Architectures for Scalable Reinforcement Learning”, Hessel et al 2021
  • “MuZero Unplugged: Online and Offline Reinforcement Learning by Planning With a Learned Model”, Schrittwieser et al 2021
  • “Learning and Planning in Complex Action Spaces”, Hubert et al 2021
  • “Scaling Scaling Laws With Board Games”, Jones2021
  • “Playing Nondeterministic Games through Planning With a Learned Model”, Willkens & Pollack2021
  • “Visualizing MuZero Models”, Vries et al 2021
  • “Combining Off and On-Policy Training in Model-Based Reinforcement Learning”, Borges & Oliveira2021
  • “Improving Model-Based Reinforcement Learning With Internal State Representations through Self-Supervision”, Scholz et al 2021
  • “On the Role of Planning in Model-Based Deep Reinforcement Learning”, Hamrick et al 2020
  • “The Value Equivalence Principle for Model-Based Reinforcement Learning”, Grimm et al 2020
  • “Measuring Progress in Deep Reinforcement Learning Sample Efficiency”, Anonymous2020
  • “Monte-Carlo Tree Search As Regularized Policy Optimization”, Grill et al 2020
  • “Continuous Control for Searching and Planning With a Learned Model”, Yang et al 2020
  • “Agent57: Outperforming the Human Atari Benchmark”, Puigdomènech et al 2020
  • “MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser et al 2019
  • “Surprising Negative Results for Generative Adversarial Tree Search”, Azizzadenesheli et al 2018
  • “TreeQN & ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning”, Farquhar et al 2017
  • “Monte Carlo Tree Search in JAX”
  • “A Clean Implementation of MuZero and AlphaZero following the AlphaZero General Framework. Train and Pit Both Algorithms against Each Other, and Investigate Reliability of Learned MuZero MDP Models.”
  • “MuZero”
  • “Learning to Search With MCTSnets”
  • “MuZero Intuition”
  • “Remaking EfficientZero (as Best I Can)”
  • “EfficientZero: How It Works”
  • “MuZero”
  • Wikipedia
• Miscellaneous
• Bibliography