Bibliography:

1. ​ ‘model-based RL’ tag

2. ​ ‘hidden-information game’ tag

3. ​ ‘offline RL’ tag

4. ​ AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning

5. ​ Job Hunt as a PhD in RL: How it Actually Happens § Reinforcement learning reflections

6. ​ Large-Scale Retrieval for Reinforcement Learning

7. ​ Boosting Search Engines with Interactive Agents

8. ​ Stochastic MuZero: Planning in Stochastic Environments with a Learned Model

9. ​ Policy improvement by planning with Gumbel

10. ​ MuZero with Self-competition for Rate Control in VP9 Video Compression

11. ​ Procedural Generalization by Planning with Self-Supervised World Models

12. ​ Mastering Atari Games with Limited Data

13. ​ Proper Value Equivalence

14. ​ Vector Quantized Models for Planning

15. ​ Muesli: Combining Improvements in Policy Optimization

16. ​ Podracer architectures for scalable Reinforcement Learning

17. ​ MuZero Unplugged: Online and Offline Reinforcement Learning by Planning with a Learned Model

18. ​ Learning and Planning in Complex Action Spaces

19. ​ Scaling Scaling Laws with Board Games

20. ​ Playing Nondeterministic Games through Planning with a Learned Model

21. ​ Visualizing MuZero Models

22. ​ Combining Off and On-Policy Training in Model-Based Reinforcement Learning

23. ​ Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision

24. ​ On the role of planning in model-based deep reinforcement learning

25. ​ The Value Equivalence Principle for Model-Based Reinforcement Learning

26. ​ Measuring Progress in Deep Reinforcement Learning Sample Efficiency

27. ​ Monte-Carlo Tree Search as Regularized Policy Optimization

28. ​ Continuous Control for Searching and Planning with a Learned Model

29. ​ Agent57: Outperforming the human Atari benchmark

30. ​ MuZero: Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model

31. ​ Surprising Negative Results for Generative Adversarial Tree Search

32. ​ TreeQN & ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

33. ​ Monte Carlo Tree Search in JAX

34. ​ 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.

35. ​ MuZero

36. ​ Learning to Search With MCTSnets

37. ​ MuZero Intuition

38. ​ b6c1341b8703a5b0a1e44ede5f00d4e5a0b02354.html

39. ​ Remaking EfficientZero (as Best I Can)

40. ​ EfficientZero: How It Works

41. ​ MuZero

42. ​ 2021-schrittwieser-figure1-mspacmanmuzerologrewardscaling.jpg

43. ​ 2020-anonymous-drlsampleefficiency-figure1-alescoresandsamplesovertime.png

44. ​ 2020-anonymous-drlsampleefficiency-figure2-dqnlevelsampleefficiencyovertime.jpg

45. ​ https://github.com/opendilab/LightZero

46. ​ https://www.reddit.com/r/reinforcementlearning/comments/zqxc12/muzero_learns_to_play_teamfight_tactics/

47. ​ d13d0162737154474d60b1ba4fb6d02fa80daf7a.html

48. ​ https://x.com/polynoamial/status/1676971503261454340

49. ​ Large-Scale Retrieval for Reinforcement Learning

50. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2206.05314%2523deepmind.html

51. ​ Boosting Search Engines with Interactive Agents

52. ​ https%253A%252F%252Fopenreview.net%252Fforum%253Fid%253D0ZbPmmB61g%2523google.html

53. ​ Policy improvement by planning with Gumbel

54. ​ Julian Schrittwieser

55. ​ https%253A%252F%252Fopenreview.net%252Fforum%253Fid%253DbERaNdoegnO%2523deepmind.html

56. ​ Procedural Generalization by Planning with Self-Supervised World Models

57. ​ Julian Schrittwieser

58. ​ Sherjil Ozair

59. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2111.01587%2523deepmind.html

60. ​ Mastering Atari Games with Limited Data

61. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2111.00210.html

62. ​ Proper Value Equivalence

63. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2106.10316%2523deepmind.html

64. ​ Vector Quantized Models for Planning

65. ​ Sherjil Ozair

66. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2106.04615%2523deepmind.html

67. ​ Podracer architectures for scalable Reinforcement Learning

68. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2104.06272%2523deepmind.html

69. ​ MuZero Unplugged: Online and Offline Reinforcement Learning by Planning with a Learned Model

70. ​ Julian Schrittwieser

71. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2104.06294%2523deepmind.html

72. ​ Visualizing MuZero Models

73. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2102.12924.html

74. ​ The Value Equivalence Principle for Model-Based Reinforcement Learning

75. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2011.03506%2523deepmind.html

76. ​ Measuring Progress in Deep Reinforcement Learning Sample Efficiency

77. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2102.04881.html

78. ​ Continuous Control for Searching and Planning with a Learned Model

79. ​ https%253A%252F%252Farxiv.org%252Fabs%252F2006.07430.html

80. ​ Agent57: Outperforming the human Atari benchmark

81. ​ https%253A%252F%252Fdeepmind.google%252Fdiscover%252Fblog%252Fagent57-outperforming-the-human-atari-benchmark%252F.html

82. ​ TreeQN & ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

83. ​ https%253A%252F%252Farxiv.org%252Fabs%252F1710.11417.html

84. Wikipedia Bibliography:

    1. ​ Monte Carlo Tree Search

    2. ​ MuZero

    3. ​ Tensor Processing Unit

    4. ​ Timothy Lillicrap

    5. ​ Thomas Hofmann

    6. ​ Yannic Kilcher

    7. ​ David Silver (programmer)

    8. ​ Pieter Abbeel

    9. ​ Yang Gao

    10. ​ Satinder Singh

    11. ​ Oriol Vinyals

    12. ​ John Quan