IJCAI 2020丨近期必读七篇【深度强化学习】论文

2020 年 9 月 28 日 学术头条

导语：

国际人工智能联合会议（International Joint Conference on Artificial Intelligence, 简称为 IJ CAI）是人工智能领域中最主要的学术会议之一，原为单数年召开，自2016年起改为每年召开。因疫情的影响, IJCAI 2 02 0将于2021年1月5日-10日在举行。

根据AMiner-IJCAI 2020词云图，小脉发现表征学习、图神经网络、深度强化学习、深度神经网络等都是今年比较火的Topic，受到了很多人的关注。今天小脉给大家分享的是IJCAI 2020七篇必读的深度强化学习（Deep Reinforcement Learning）相关论文。

1. 论文名称：Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

论文链接：

https://www.aminer.cn/pub/5ef96b048806af6ef2772111/efficient-deep-reinforcement-learning-via-adaptive-policy-transfer?conf=ijcai2020

作者：Tianpei Yang、Jianye Hao、Zhaopeng Meng、Zongzhang Zhang、Yujing Hu、Yingfeng Chen、Changjie Fan、Weixun Wang、Wulong Liu、Zhaodong Wang、Jiajie Peng

简介：

· The authors propose a Policy Transfer Framework (PTF) which can efficiently select the optimal source policy and exploit the useful information to facilitate the target task learning.

· PTF efficiently avoids negative transfer through terminating the exploitation of current source policy and selects another one adaptively.

· PTF can be combined with existing deep DRL methods.

· Experimental results show PTF efficiently accelerates the learning process of existing state-ofthe-art DRL methods and outperforms previous policy reuse approaches.

2. 论文名称：KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge

论文链接：

https://www.aminer.cn/pub/5e4d083f3a55ac8cfd770c23/kogun-accelerating-deep-reinforcement-learning-via-integrating-human-suboptimal-knowledge?conf=ijcai2020

作者：Zhang Peng、Jianye Hao、Wang Weixun、Tang Hongyao、Ma Yi、Duan Yihai、Zheng Yan

简介：

· The authors propose a novel policy network framework called KoGuN to leverage human knowledge to accelerate the learning process of RL agents.

· The authors firstly evaluate the algorithm on four tasks in Section 4.1 : CartP ole [Barto and Sutton, 1982], LunarLander and LunarLanderContinuous in OpenAI Gym [Brockman et al, 2016] and F lappyBird in PLE [Tasfi, 2016].

· The authors show the effectiveness and robustness of KoGuN in sparse reward setting in Section 4.2.

· For PPO without KoGuN, the authors use a neural network with two full-connected hidden layers as policy approximator.

· For KoGuN with normal network (KoGuN-concat) as refine module, the authors use a neural network with two full-connected hidden layers for the refine module.

· For KoGuN with hypernetworks (KoGuN-hyper), the authors use hypernetworks to generate a refine module with one hidden layer.

· All hidden layers described above have 32 units. w1 is set to 0.7 at beginning and decays to 0.1 in the end of training phase

3. 论文名称：Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning

论文链接：

https://www.aminer.cn/pub/5ef96b048806af6ef277219d/generating-behavior-diverse-game-ais-with-evolutionary-multi-objective-deep-reinforcement-learning?conf=ijcai2020

作者：Ruimin Shen、Yan Zheng、Jianye Hao、Zhaopeng Meng、Yingfeng Chen、Changjie Fan、Yang Liu

简介：

· This paper proposes EMOGI, aiming to efficiently generate behavior-diverse Game AIs by leveraging EA, PMOO and DRL.

· Empirical results show the effectiveness of EMOGI in creating diverse and complex behaviors.

· To deploy AIs in commercial games, the robustness of the generated AIs is worth investigating as future work [Sun et al, 2020]

4. 论文名称：Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning

论文链接：

https://www.aminer.cn/pub/5eda19d991e01187f5d6db49/solving-hard-ai-planning-instances-using-curriculum-driven-deep-reinforcement-learning?conf=ijcai2020

作者：Feng Dieqiao、Gomes Carla P.、Selman Bart

简介：

· The authors presented a framework based on deep RL for solving hard combinatorial planning problems in the domain of Sokoban.

· The authors showed the effectiveness of the learning based planning strategy by solving hard Sokoban instances that are out of reach of previous search-based solution techniques, including methods specialized for Sokoban.

· Since Sokoban is one of the hardest challenge domains for current AI planners, this work shows the potential of curriculumbased deep RL for solving hard AI planning tasks.

5. 论文名称：I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations

论文链接：

https://www.aminer.cn/pub/5ef96b048806af6ef2772128/i-r-promoting-deep-reinforcement-learning-by-the-indicator-for-expressive-representations?conf=ijcai2020

作者：Xufang Luo、Qi Meng、Di He、Wei Chen、Yunhong Wang

简介：

· The authors mainly study the relationship between representations and performance of the DRL agents.

· The authors define the NSSV indicator, i.e, the smallest number of significant singular values, as a measurement for learning representations, the authors verify the positive correlation between NSSV and the rewards, and further propose a novel method called I4R, to improve DRL algorthims via adding the corresponding regularization term to enhance NSSV.

· The authors show the proposed method I4R based on exploratory experiments, including 3 parts, i.e., observations, the proposed indicator NSSV, and the novel algorithm I4R.

6. 论文名称：Rebalancing Expanding EV Sharing Systems with Deep Reinforcement Learning

论文链接：

https://www.aminer.cn/pub/5ef96b048806af6ef2772092/rebalancing-expanding-ev-sharing-systems-with-deep-reinforcement-learning?conf=ijcai2020

作者：Man Luo、Wenzhe Zhang、Tianyou Song、Kun Li、Hongming Zhu、Bowen Du 、Hongkai Wen

简介：

· The authors study the incentive-based rebalancing for continuous expanding EV sharing systems.

· The authors design a simulator to simulate the operation of EV sharing systems, which is calibrated with real data from an actual EV sharing system for a year.

· Extensive experiments have shown that the proposed approach significantly outperforms the baselines and state-of-the-art in both satisfied demand rate and net revenue, and is robust to different levels of system expansion dynamics.

· The authors show that the proposed approach performs consistently with different charging time and EV range.

7. 论文名称：Independent Skill Transfer for Deep Reinforcement Learning

论文链接：

https://www.aminer.cn/pub/5ef96b048806af6ef2772129/independent-skill-transfer-for-deep-reinforcement-learning?conf=ijcai2020

作者：Qiangxing Tian、Guanchu Wang、Jinxin Liu、Donglin Wang、Yachen Kang

简介：

· Deep reinforcement learning (DRL) has wide applications in various challenging fields, such as real-world visual navigation [Zhu et al, 2017], playing games [Silver et al, 2016] and robotic controls [Schulman et al, 2015]

· In this work , the authors propose to learn independent skills for efficient skill transfer, where the learned primitive skills with strong correlation are decomposed into independent skills

· We take the eigenvalues in Figure 1 as an example: for the case of 6 primitive skills, |Z| = 3 is reasonable since more than 98% component of primitive actions can be represented by three independent components

· Effective observation collection and independent skills guarantee the success of low-dimension skill transfer