強化学習の系図

強化学習を一旦掘り下げられるよう整理したかったので，

• いつ
• だれが
• (どんな問題を解いたアルゴリズムで)
• 何の略称で
• 親は誰なのか

系図を作ってまとめてみました．(取り違えているかもしれません)

図: 主要な強化学習アルゴリズムの系図(左上の数字は誕生年)

TD(λ)(Sutton, 1984;1988)

• Temporal Differences
• Sutton, Richard S. "Learning to predict by the methods of temporal differences." Machine learning 3.1 (1988): 9-44.

Q学習(Watkins, 1989)

• Watkins, Christopher JCH, and Peter Dayan. "Q-learning." Machine learning 8.3-4 (1992): 279-292.

REINFORCE(Williams, 1992)

• REward Increment = Nonnegative Factor Offset Reinforcement
• Williams, Ronald J. "Simple statistical gradient-following algorithms for connectionist reinforcement learning." Machine learning 8.3-4 (1992): 229-256.

SARSA(Rummery, 1994)

• State-Action-Reward-State-Actionの略称(論文の注釈で登場)
• Rummery, Gavin A., and Mahesan Niranjan. On-line Q-learning using connectionist systems. Vol. 37. Cambridge, England: University of Cambridge, Department of Engineering, 1994.
• cf. sugulu, Qiita -【強化学習初心者向け】シンプルな実装例で学ぶSARSA法およびモンテカルロ法【CartPoleで棒立て：1ファイルで完結】

CEM(Rubenstein, 1997)

• Cross Entropy Method
• 進化的アルゴリズム
• 参考1: http://web.mit.edu/6.454/www/www_fall_2003/gew/CEtutorial.pdf
• 参考2: https://esc.fnwi.uva.nl/thesis/centraal/files/f2110275396.pdf
• 参考3: http://learning.mpi-sws.org/mlss2016/slides/2016-MLSS-RL.pdf

AL(Baird Ⅲ, 1999)

• Advantage Learning
• Baird III, Leemon C. Reinforcement learning through gradient descent. No. CMU-CS-99-132. CARNEGIE-MELLON UNIV PITTSBURGH PA DEPT OF COMPUTER SCIENCE, 1999.

AC(Sutton et al., 2000)

• Actor-Critic
• Sutton, Richard S., et al. "Policy gradient methods for reinforcement learning with function approximation." Advances in neural information processing systems. 2000.

NFQ(Riedmiller, 2005)

• Neural Fitted Q Iteration
• Riedmiller, Martin. "Neural fitted Q iteration–first experiences with a data efficient neural reinforcement learning method." European Conference on Machine Learning. Springer, Berlin, Heidelberg, 2005.

REPS(Peters et al., 2010)

• Relative Entropy Policy Search
• Peters, Jan, Katharina Mülling, and Yasemin Altun. "Relative Entropy Policy Search." AAAI. 2010.

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DQN(Mnih et al., 2013)

• Deep Q-Networks
• Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).
• cf. sugulu, Qiita -【強化学習初心者向け】シンプルな実装例で学ぶQ学習、DQN、DDQN【CartPoleで棒立て：1ファイルで完結、Kearas使用】

DPG(Silver et al., 2014)

• Deterministic Policy Gradient
• Silver, David, et al. "Deterministic policy gradient algorithms." ICML. 2014.

DDQN(Hasselt et al., 2015)

• Double DQN
• Van Hasselt, Hado, Arthur Guez, and David Silver. "Deep Reinforcement Learning with Double Q-Learning." AAAI. Vol. 2. 2016.
• cf. sugulu, Qiita -【強化学習中級者向け】実装例から学ぶDueling Network DQN 【CartPoleで棒立て：1ファイルで完結】

Dueling DQN(Wang et al., 2015)

• Double DQNの進化版
• Wang, Ziyu, et al. "Dueling network architectures for deep reinforcement learning." arXiv preprint arXiv:1511.06581 (2015).

PAL(Bellemare et al., 2015)

• Persistent Average Learning
• Wang, Ziyu, et al. "Dueling network architectures for deep reinforcement learning." arXiv preprint arXiv:1511.06581 (2015).

TRPO(Schulman et al., 2015)

• Trust Region Policy Optimization
• Schulman, John, et al. "Trust region policy optimization." International Conference on Machine Learning. 2015.

DDPG(Lillicrap et al., 2015)

• Deep DPG
• Lillicrap, Timothy P., et al. "Continuous control with deep reinforcement learning." arXiv preprint arXiv:1509.02971 (2015).

PER(Schual et al., 2015)

• Prioritized Experience Replay
• Schaul, Tom, et al. "Prioritized experience replay." arXiv preprint arXiv:1511.05952 (2015).
• sugulu, 【強化学習中級者向け】実装例から学ぶ優先順位付き経験再生 prioritized experience replay DQN 【CartPoleで棒立て：1ファイルで完結】

Gorila(Nair et al., 2016)

• General Reinforcement Learning Architecture
• Mnih, Volodymyr, et al. "Asynchronous methods for deep reinforcement learning." International conference on machine learning. 2016.

NAF(Gu et al., 2016)

• Normalized Advantage Function
• 行動空間が連続な問題でも解けるようDouble DQNを拡張
• Gu, Shixiang, et al. "Continuous deep q-learning with model-based acceleration." International Conference on Machine Learning. 2016.

A3C(Mnih et al., 2016)

• Asynchronous Advantage Actor-Critic
• Mnih, Volodymyr, et al. "Asynchronous methods for deep reinforcement learning." International conference on machine learning. 2016.
• cf. sugulu, Qiita -【強化学習】実装しながら学ぶA3C【CartPoleで棒立て：1ファイルで完結】

A2C(Mnih et al., 2016)

• Advantage Actor-Critic
• アイデア: https://blog.openai.com/baselines-acktr-a2c/
• Mnih, Volodymyr, et al. "Asynchronous methods for deep reinforcement learning." International conference on machine learning. 2016.

UNREAL(Jarderberg et al., 2016)

• UNsupervised REinforcement and Auxiliary Learning
• Jaderberg, Max, et al. "Reinforcement learning with unsupervised auxiliary tasks." arXiv preprint arXiv:1611.05397 (2016).

GAIL(Ho et al., 2016)

• Generative Adversarial Imitation Learning
• Ho, Jonathan, and Stefano Ermon. "Generative adversarial imitation learning." Advances in Neural Information Processing Systems. 2016.

ACER(Wang et al., 2016)

• Actor-Critic with Experience Replay
• Wang, Ziyu, et al. "Sample efficient actor-critic with experience replay." arXiv preprint arXiv:1611.01224 (2016).

HER(Andrychowicz et al., 2017)

• Hindsight Experience Replay
• Andrychowicz, Marcin, et al. "Hindsight experience replay." Advances in Neural Information Processing Systems. 2017.
• cf. ishizakiiii, Qiita - 失敗からも学ぶ 強化学習 HERのアルゴリズムを理解して、OpenAI Gymの新しいロボットで試してみた

C51(Bellemare et al., 2017)

• Rainbowの特殊系
• A Distributional Perspective on Reinforcement Learning
• Bellemare, Marc G., Will Dabney, and Rémi Munos. "A distributional perspective on reinforcement learning." arXiv preprint arXiv:1707.06887 (2017).

Rainbow (Hessel et al., 2017)

• Hessel, Matteo, et al. "Rainbow: Combining improvements in deep reinforcement learning." arXiv preprint arXiv:1710.02298 (2017).
• cf. Jun Okumura, SlideShare - DQNからRainbowまで 〜深層強化学習の最新動向〜

PPO(Schulman et al., 2017)

• Proximal Policy Optimization
• Schulman, John, et al. "Proximal policy optimization algorithms." arXiv preprint arXiv:1707.06347 (2017).
• cf. sugulu, Qiita -【強化学習】実装しながら学ぶPPO【CartPoleで棒立て：1ファイルで完結】

Song et al., 2018より

ACKTR(Wu et al., 2017)

• Actor Critic using Kronecker-Factored Trust Region
• Wu, Yuhuai, et al. "Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation." Advances in neural information processing systems. 2017.

SDQN(Metz et al., 2017)

• Sequential DQN
• Metz, Luke, et al. "Discrete sequential prediction of continuous actions for deep RL." arXiv preprint arXiv:1705.05035 (2017).

QR-DQN(Dabney et al., 2017)

• Quantile Regression DQN
• Dabney, Will, et al. "Distributional reinforcement learning with quantile regression." arXiv preprint arXiv:1710.10044 (2017).

IQN(Dabney et al., 2018)

• Implicit Quantile Network
• Dabney, Will, et al. "Implicit Quantile Networks for Distributional Reinforcement Learning." arXiv preprint arXiv:1806.06923 (2018).

Ape-X(Horgan et al. 2018)

• DISTRIBUTED PRIORITIZED EXPERIENCE REPLAY
• 参考: 【深層強化学習】『2018年最強手法(?)』Ape-X 実装・解説, Qiita.

References

• GitHub - Deep Reinforcement Learning Papers