深度強化學習的18個關鍵問題
from: https://zhuanlan.zhihu.com/p/32153603
深度強化學習的問題在哪里？未來怎么走？哪些方面可以突破？
這兩天我閱讀了兩篇篇猛文A Brief Survey of Deep Reinforcement Learning 和 Deep Reinforcement Learning: An Overview ，作者排山倒海的引用了200多篇文獻，闡述強化學習未來的方向。原文歸納出深度強化學習中的常見科學問題，並列出了目前解法與相關綜述，我在這里做出整理，抽取了相關的論文。
這里精選18個關鍵問題，涵蓋空間搜索、探索利用、策略評估、內存使用、網絡設計、反饋激勵等等話題。本文精選了73篇論文（其中2017年論文有27篇，2016年論文有21篇）為了方便閱讀，原標題放在文章最后，可以根據索引找到。
TODO list：文章內容還不夠充實，但是論文是全的。未來一段時間會把論文的鏈接找齊，下載好然后打個包傳到百度雲上，預計一兩天完成。（2017/12/19）
問題一：預測與策略評估
prediction, policy evaluation
萬變不離其宗，Temporal Difference方法仍然是策略評估的核心哲學【Sutton 1988】。TD的拓展版本和她本身一樣鼎鼎大名——1992年的Q-learning與2015年的DQN。
美中不足，TD Learning中很容易出現Over-Estimate（高估）問題，具體原因如下：
The max operator in standard Q-learning and DQN use the same values both to select and to evaluate an action. —— van Hasselt
曠世猛將van Hasselt先生很喜歡處理Over-Estimate問題，他先搞出一個Double Q-learning【van Hasselt 2010】大鬧NIPS，六年后搞出深度學習版本的Double DQN【van Hasselt 2016a】！
問題二：控制與最佳策略選擇
control, finding optimal policy
目前解法有三個流派，一圖勝千言：
台大李宏毅教授的Slide
- 最傳統的方法是Value-Based，就是選擇有最優Value的Action。最經典方法有：Q-learning 【Watkins and Dayan 1992】、SARSA 【Sutton and Barto 2017】
- 后來Policy-Based方法引起注意，最開始是REINFORCE算法【Williams 1992】，后來策略梯度Policy Gradient【Sutton 2000】出現。
- 最時行的Actor-Critic 【Barto et al 1983】把兩者做了結合。樓上Sutton老爺子的好學生、AlphaGo的總設計師David Silver同志提出了Deterministic Policy Gradient，表面上是PG，實際講了一堆AC，這個改進史稱DPG【Silver 2014】
Actor-Critic的循環促進過程
問題三：不穩定與不收斂問題
Instability and Divergence when combining off-policy，function approximation，bootstrapping
早在1997年Tsitsiklis就證明了如果Function Approximator采用了神經網絡這種非線性的黑箱，那么其收斂性和穩定性是無法保證的。
分水嶺論文Deep Q-learning Network【Mnih et al 2013】中提到：雖然我們的結果看上去很好，但是沒有任何理論依據（原文很狡猾的反過來說一遍）。
This suggests that, despite lacking any theoretical convergence guarantees, our method is able to train large neural networks using a reinforcement learning signal and stochastic gradient descent in stable manner
征服Atari游戲的DQN
DQN的改良主要依靠兩個Trick：
- 經驗回放【Lin 1993】（雖然做不到完美的獨立同分布，但還是要盡力減少數據之間的關聯性）
- Target Network【Mnih 2015】(Estimated Network和Target Network不能同時更新參數，應該另設Target Network以保證穩定性)
Since the network Q being updated is also used in calculating the target value, the Q update is prone to divergence.（為什么我們要用Target Network）
下面幾篇論文都是DQN相關話題的：
- 經驗回放升級版：Prioritized Experience Replay 【Schaul 2016】
- 更好探索策略 【Osband 2016】
- DQN加速 【He 2017a】
- 通過平均減少方差與不穩定性Averaged-DQN 【Anschel 2017】
下面跳出DQN的范疇——
Duel DQN【Wang 2016c】（ICML2016最佳論文）
Tips：閱讀此文請掌握DQN、Double DQN、Prioritized Experience Replay這三個背景。
異步算法A3C 【Mnih 2016】
TRPO(Trust Region Policy Optimization)【Schulman 2015】
Distributed Proximal Policy Optimization 【Heess 2017】
Policy gradient與Q-learning 的結合【O'Donoghue 2017、Nachum 2017、 Gu 2017、Schulman 2017】
GTD 【Sutton 2009a、Sutton 2009b、Mahmood 2014】
Emphatic-TD 【Sutton 2016】
問題四：End-to-End下的訓練感知與控制
train perception and control jointly end-to-end
現有解法是Guided Policy Search 【Levine et al 2016a】
問題五：數據利用效率
data/sample efficiency
現有解法有：
Q-learning與Actor-Critic
經驗回放下的actor-critic 【Wang et al 2017b】
PGQ，policy gradient and Q-learning 【O'Donoghue et al 2017】
Q-Prop, policy gradient with off-policy critic 【Gu et al 2017】
return-based off-policy control, Retrace 【Munos et al 2016】, Reactor 【Gruslyset al 2017】
learning to learn, 【Duan et al 2017、Wang et al 2016a、Lake et al 2015】
問題六：無法取得激勵
reward function not available
現有解法基本上圍繞模仿學習
吳恩達的逆強化學習【Ng and Russell 2000)】
learn from demonstration 【Hester et al 2017】
imitation learning with GANs 【Ho and Ermon 2016、Stadie et al 2017】 （其TensorFlow實現在imitation）
train dialogue policy jointly with reward model 【Su et al 2016b】
問題七：探索-利用問題（最經典的問題）
exploration-exploitation tradeoff
現有解法有：
unify count-based exploration and intrinsic motivation 【Bellemare et al 2017】
under-appreciated reward exploration 【Nachum et al 2017)】
deep exploration via bootstrapped DQN 【Osband et al 2016)】
variational information maximizing exploration 【Houthooft et al 2016】
問題八：基於模型的學習
model-based learning
現有解法：
Sutton老爺子教科書里的經典安利：Dyna-Q 【Sutton 1990】
model-free與model-based的結合使用【Chebotar et al 2017】
問題九：無模型規划
model-free planning
比較新的解法有兩個：
- Value Iteration Networks【Tamar et al 2016】是勇奪NIPS2016最佳論文頭銜的猛文，知乎上已經有專門的文章解說了：Value iteration Network，VIN的TensorFlow實現在tensorflow-value-iteration-networks。
- DeepMind的Silver大神發表的Predictron方法 【Silver et al 2016b】，其TensorFlow實現是predictron。
問題十：它山之石可以攻玉
focus on salient parts
大神曾經說過：
伯克利人工智能方向的博士生，入學一年以后資格考試要考這幾個內容：
強化學習和Robotics、 統計和概率圖模型、 計算機視覺和圖像處理、 語音和自然語言處理、 核方法及其理論、 搜索，CSP，邏輯，Planning等
如果真的想做人工智能，建議都了解一下，不是說都要搞懂搞透，但是至少要達到開會的時候和人在poster前面談笑風生不出錯的程度吧。
因此，一個很好的思路是從計算機視覺與自然語言處理領域汲取靈感，例如下文中將會提到的unsupervised auxiliary learning方法借鑒了RNN+LSTM中的大量操作。
下面是CV和NLP方面的幾個簡介：物體檢測 【Mnih 2014】、機器翻譯 【Bahdanau 2015】、圖像標注【Xu 2015】、用Attention代替CNN和RNN【Vaswani 2017】等等。
問題十一：長時間數據儲存
data storage over long time, separating from computation
最出名的解法是在Nature上大秀一把的Differentiable Neural Computer【Graves et al 2016】
問題十二：無回報訓練
benefit from non-reward training signals in environments
現有解法圍繞着無監督學習開展
Horde 【Sutton et al 2011】
極其優秀的工作：unsupervised reinforcement and auxiliary learning 【Jaderberg et al 2017】
learn to navigate with unsupervised auxiliary learning 【Mirowski et al 2017】
大名鼎鼎的GANs 【Goodfellow et al 2014】
問題十三：跨領域學習
learn knowledge from different domains
現有解法全部圍繞遷移學習走 【Taylor and Stone, 2009、Pan and Yang 2010、Weiss et al 2016】，learn invariant features to transfer skills 【Gupta et al 2017】
問題十四：有標簽數據與無標簽數據混合學習
benefit from both labelled and unlabelled data
現有解法全部圍繞半監督學習 【Zhu and Goldberg 2009】
learn with MDPs both with and without reward functions 【Finn et al 2017)】
learn with expert's trajectories and those may not from experts 【Audiffren et al 2015】
問題十五：多層抽象差分空間的表示與推斷
learn, plan, and represent knowledge with spatio-temporal abstraction at multiple levels
現有解法：多層強化學習 【Barto and Mahadevan 2003】
strategic attentive writer to learn macro-actions 【Vezhnevets et al 2016】
integrate temporal abstraction with intrinsic motivation 【Kulkarni et al 2016】
stochastic neural networks for hierarchical RL 【Florensa et al 2017】
lifelong learning with hierarchical RL 【Tessler et al 2017】
問題十六：不同任務環境快速適應
adapt rapidly to new tasks
現有解法基本上是learn to learn
learn a flexible RNN model to handle a family of RL tasks 【Duan et al 2017、Wang et al 2016a】
one/few/zero-shot learning 【Duan et al 2017、Johnson et al 2016、 Kaiser et al 2017b、Koch et al 2015、Lake et al 2015、Li and Malik 2017、Ravi and Larochelle, 2017、Vinyals et al 2016)
問題十七：巨型搜索空間
gigantic search space
現有解法依然是蒙特卡洛搜索，詳情可以參考初代AlphaGo的實現【Silver et al 2016a】
問題十八：神經網絡架構設計
（neural networks architecture design ）
現有的網絡架構搜索方法【Baker et al 2017、Zoph and Le 2017】，其中Zoph的工作分量非常重。
新的架構有【Kaiser et al 2017a、Silver et al 2016b、Tamar et al 2016、Vaswani et al 2017、Wang et al 2016c】
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