国际人工智能联合会议（International Joint Conference on Artificial Intelligence, 简称为 IJCAI）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 2020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
2. 论文名称：KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge
3. 论文名称：Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning
4. 论文名称：Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
5. 论文名称：I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations
6. 论文名称：Rebalancing Expanding EV Sharing Systems with Deep Reinforcement Learning
7. 论文名称：Independent Skill Transfer for Deep Reinforcement Learning