“‘AlphaGo’ Tag”,2019-09-09 (CNN, NN sampling, AI chess, model-based RL, MARL, RL scaling; backlinks)⁠:

Bibliography for tag reinforcement-learning/model/alphago, most recent first: 4 related tags, 104 annotations, & 29 links (parent).

• See Also
• Links
  • “Can Go AIs Be Adversarially Robust?”, Tseng et al 2024
  • “Artificial Intelligence for Retrosynthetic Planning Needs Both Data and Expert Knowledge”, Strieth-Kalthoff et al 2024
  • “Gold-Medalist Coders Build an AI That Can Do Their Job for Them: A New Startup Called Cognition AI Can Turn a User’s Prompt into a Website or Video Game”, Vance2024
  • “Beyond A^✱: Better Planning With Transformers via Search Dynamics Bootstrapping (Searchformer)”, Lehnert et al 2024
  • “Everything of Thoughts: Defying the Law of Penrose Triangle for Thought Generation”, Ding et al 2023
  • “Bridging the Human-AI Knowledge Gap: Concept Discovery and Transfer in AlphaZero”, Schut et al 2023
  • “Diversifying AI: Towards Creative Chess With AlphaZero (AZ_db)”, Zahavy et al 2023
  • “Self-Play Reinforcement Learning Guides Protein Engineering”, Wang et al 2023c
  • “Evaluating Superhuman Models With Consistency Checks”, Fluri et al 2023
  • “BetaZero: Belief-State Planning for Long-Horizon POMDPs Using Learned Approximations”, Moss et al 2023
  • “Who Will You Be After ChatGPT Takes Your Job? Generative AI Is Coming for White-Collar Roles. If Your Sense of worth Comes from Work—What’s Left to Hold on To?”, Thomas2023
  • “AlphaZe∗∗: AlphaZero-Like Baselines for Imperfect Information Games Are Surprisingly Strong”, Blüml et al 2023
  • “Solving Math Word Problems With Process & Outcome-Based Feedback”, Uesato et al 2022
  • “Are AlphaZero-Like Agents Robust to Adversarial Perturbations?”, Lan et al 2022
  • “Adversarial Policies Beat Superhuman Go AIs”, Wang et al 2022
  • “Large-Scale Retrieval for Reinforcement Learning”, Humphreys et al 2022
  • “Newton’s Method for Reinforcement Learning and Model Predictive Control”, Bertsekas2022
  • “HTPS: HyperTree Proof Search for Neural Theorem Proving”, Lample et al 2022
  • “CrossBeam: Learning to Search in Bottom-Up Program Synthesis”, Shi et al 2022
  • “Policy Improvement by Planning With Gumbel”, Danihelka et al 2022
  • “Formal Mathematics Statement Curriculum Learning”, Polu et al 2022
  • “Player of Games”, Schmid et al 2021
  • “Ν-SDDP: Neural Stochastic Dual Dynamic Programming”, Dai et al 2021
  • “Acquisition of Chess Knowledge in AlphaZero”, McGrath et al 2021
  • “Evaluating Model-Based Planning and Planner Amortization for Continuous Control”, Byravan et al 2021
  • “Scalable Online Planning via Reinforcement Learning Fine-Tuning”, Fickinger et al 2021
  • “Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control”, Bertsekas2021
  • “How Does AI Improve Human Decision-Making? Evidence from the AI-Powered Go Program”, Choi et al 2021
  • “Train on Small, Play the Large: Scaling Up Board Games With AlphaZero and GNN”, Ben-Assayag & El-Yaniv2021
  • “Neural Tree Expansion for Multi-Robot Planning in Non-Cooperative Environments”, Riviere et al 2021
  • “Scaling Scaling Laws With Board Games”, Jones2021
  • “OLIVAW: Mastering Othello without Human Knowledge, nor a Fortune”, Norelli & Panconesi2021
  • “Transfer of Fully Convolutional Policy-Value Networks Between Games and Game Variants”, Soemers et al 2021
  • “Investment vs. Reward in a Competitive Knapsack Problem”, Neumann & Gros2021
  • “Solving Mixed Integer Programs Using Neural Networks”, Nair et al 2020
  • “Monte-Carlo Graph Search for AlphaZero”, Czech et al 2020
  • “Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search”, Lan et al 2020
  • “Assessing Game Balance With AlphaZero: Exploring Alternative Rule Sets in Chess”, Tomašev et al 2020
  • “Learning Personalized Models of Human Behavior in Chess”, McIlroy-Young et al 2020
  • “Learning Compositional Neural Programs for Continuous Control”, Pierrot et al 2020
  • “ReBeL: Combining Deep Reinforcement Learning and Search for Imperfect-Information Games”, Brown et al 2020
  • “Monte-Carlo Tree Search As Regularized Policy Optimization”, Grill et al 2020
  • “Tackling Morpion Solitaire With AlphaZero-Like Ranked Reward Reinforcement Learning”, Wang et al 2020
  • “Aligning Superhuman AI With Human Behavior: Chess As a Model System”, McIlroy-Young et al 2020
  • “Neural Machine Translation With Monte-Carlo Tree Search”, Parker & Chen2020
  • “Real World Games Look Like Spinning Tops”, Czarnecki et al 2020
  • “Approximate Exploitability: Learning a Best Response in Large Games”, Timbers et al 2020
  • “Accelerating and Improving AlphaZero Using Population Based Training”, Wu et al 2020
  • “Self-Play Learning Without a Reward Metric”, Schmidt et al 2019
  • “(Yonhap Interview) Go Master Lee Says He Quits—Unable to Win over AI Go Players”, Agency2019
  • “MuZero: Mastering Atari, Go, Chess and Shogi by Planning With a Learned Model”, Schrittwieser et al 2019
  • “Multiplayer AlphaZero”, Petosa & Balch2019
  • “Global Optimization of Quantum Dynamics With AlphaZero Deep Exploration”, Dalgaard et al 2019
  • “Learning Compositional Neural Programs With Recursive Tree Search and Planning”, Pierrot et al 2019
  • “Π-IW: Deep Policies for Width-Based Planning in Pixel Domains”, Junyent et al 2019
  • “Policy Gradient Search: Online Planning and Expert Iteration without Search Trees”, Anthony et al 2019
  • “AlphaX: EXploring Neural Architectures With Deep Neural Networks and Monte Carlo Tree Search”, Wang et al 2019
  • “Minigo: A Case Study in Reproducing Reinforcement Learning Research”, Anonymous2019
  • “Α-Rank: Multi-Agent Evaluation by Evolution”, Omidshafiei et al 2019
  • “Accelerating Self-Play Learning in Go”, Wu2019
  • “ELF OpenGo: An Analysis and Open Reimplementation of AlphaZero”, Tian et al 2019
  • “Bayesian Optimization in AlphaGo”, Chen et al 2018
  • “A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go through Self-Play”, Silver et al 2018
  • “Deep Reinforcement Learning”, Li2018
  • “AlphaSeq: Sequence Discovery With Deep Reinforcement Learning”, Shao et al 2018
  • “ExIt-OOS: Towards Learning from Planning in Imperfect Information Games”, Kitchen & Benedetti2018
  • “Has Dynamic Programming Improved Decision Making?”, Rust2018
  • “Surprising Negative Results for Generative Adversarial Tree Search”, Azizzadenesheli et al 2018
  • “Improving Width-Based Planning With Compact Policies”, Junyent et al 2018
  • “Dual Policy Iteration”, Sun et al 2018
  • “Solving the Rubik’s Cube Without Human Knowledge”, McAleer et al 2018
  • “Feedback-Based Tree Search for Reinforcement Learning”, Jiang et al 2018
  • “A Tree Search Algorithm for Sequence Labeling”, Lao et al 2018
  • “Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations”, Bertsekas2018
  • “Sim-To-Real Optimization of Complex Real World Mobile Network With Imperfect Information via Deep Reinforcement Learning from Self-Play”, Tan et al 2018
  • “Learning to Search With MCTSnets”, Guez et al 2018
  • “M-Walk: Learning to Walk over Graphs Using Monte Carlo Tree Search”, Shen et al 2018
  • “Mastering Chess and Shogi by Self-Play With a General Reinforcement Learning Algorithm”, Silver et al 2017
  • “AlphaGo Zero: Mastering the Game of Go without Human Knowledge”, Silver et al 2017
  • “Self-Taught AI Is Best yet at Strategy Game Go”, Gibney2017
  • “DeepMind’s Latest AI Breakthrough Is Its Most Important Yet: Google-Owned DeepMind’s Go-Playing Artificial Intelligence Can Now Learn without Human Help… or Data”, Burgess2017
  • “Learning Generalized Reactive Policies Using Deep Neural Networks”, Groshev et al 2017
  • “Learning to Plan Chemical Syntheses”, Segler et al 2017
  • “Thinking Fast and Slow With Deep Learning and Tree Search”, Anthony et al 2017
  • “DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker”, Moravčík et al 2017
  • “Mastering the Game of Go With Deep Neural Networks and Tree Search”, Silver et al 2016
  • “Giraffe: Using Deep Reinforcement Learning to Play Chess”, Lai2015
  • “Algorithmic Progress in Six Domains”, Grace2013
  • “Reinforcement Learning As Classification: Leveraging Modern Classifiers”, Lagoudakis & Parr2003
  • “Deep-Learning the Hardest Go Problem in the World”
  • “Learning From Scratch by Thinking Fast and Slow With Deep Learning and Tree Search”
  • “Acquisition of Chess Knowledge in AlphaZero”
  • “Leela Chess Zero: AlphaZero for the PC”
  • “The Future Is Here – AlphaZero Learns Chess”
  • “Trading Off Compute in Training and Inference”
  • “Trading Off Compute in Training and Inference § MCTS Scaling”
  • “Beyond the Board: Exploring AI Robustness Through Go”
  • “Monte Carlo Tree Search in JAX”
  • “An Open-Source Implementation of the AlphaGoZero Algorithm”
  • “Adversarial Policies in Go”
  • “The 3 Tricks That Made AlphaGo Zero Work”
  • “AlphaGo Zero and the Foom Debate”
  • “How to Build Your Own AlphaZero AI Using Python and Keras”, Foster2024
  • “Reading the Tea Leaves: Expert End-Users Explaining the Unexplainable”
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    • alpha-zero-optimization alpha-chess-strategies bayesian-alpha exploration-robotics ai-knowledge-transfer chess-diversification alpha-zero
    • chess-ai
    • self-play
    • game-mastering
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