From 2c884b2d70c83be4a3692c0816582bf7eb87bb28 Mon Sep 17 00:00:00 2001 From: Jin0932 Date: Sun, 9 Feb 2020 17:01:06 +0800 Subject: [PATCH] updating paperdaily issue#14,issue#15 --- DRL-PaperDaily/README.md | 27 ++++++++++++++++++++++++--- 1 file changed, 24 insertions(+), 3 deletions(-) diff --git a/DRL-PaperDaily/README.md b/DRL-PaperDaily/README.md index dca41b9..c7ff26d 100644 --- a/DRL-PaperDaily/README.md +++ b/DRL-PaperDaily/README.md @@ -3,9 +3,30 @@ > This document used to display the latest papers about Deep Reinforcement Learning, -### Continuous updating....... +### Continuous updating...... -Issue# 11：2020-1-20 +Issue# 15：2020-2-20 +---- +1. [Locally Private Distributed Reinforcement Learning](https://arxiv.org/abs/2001.11718) by Hajime Ono, Tsubasa Takahashi +2. [Effective Diversity in Population-Based Reinforcement Learning](https://arxiv.org/abs/2002.00632) by Jack Parker-Holder, Stephen Roberts +3. [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://arxiv.org/abs/2002.00444) by B Ravi Kiran, Patrick Pérez +4. [Attractive or Faithful? Popularity-Reinforced Learning for Inspired Headline Generation](https://arxiv.org/abs/2002.02095) by Yun-Zhu Song, AAAI 2020 +5. [Asymptotically Efficient Off-Policy Evaluation for Tabular Reinforcement Learning](https://arxiv.org/abs/2001.10742) by Ming Yin, Yu-Xiang Wang (Includes appendix. Accepted for AISTATS 2020) + + +Issue# 14：2020-2-10 +---- +1. [Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping](https://arxiv.org/abs/2001.07527) by Eugenio Bargiacchi, Ann Nowé +2. [Reinforcement Learning with Probabilistically Complete Exploration](https://arxiv.org/abs/2001.06940) by Philippe Morere, Fabio Ramos +3. [Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory](https://arxiv.org/abs/2001.06487) by Yunlong Lu, Kai Yan +4. [Local Policy Optimization for Trajectory-Centric Reinforcement Learning](https://arxiv.org/abs/2001.08092) by Patrik Kolaric, Daniel Nikovski +5. [On Simple Reactive Neural Networks for Behaviour-Based Reinforcement Learning](https://arxiv.org/abs/2001.07973) by Ameya Pore, Gerardo Aragon-Camarasa +6. [Graph Constrained Reinforcement Learning for Natural Language Action Spaces](https://arxiv.org/abs/2001.08837) by Prithviraj Ammanabrolu, Matthew Hausknecht(Accepted to ICLR 2020) +7. [Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning](https://arxiv.org/abs/2001.09684) by Inaam Ilahi, Dusit Niyato +8. [Active Task-Inference-Guided Deep Inverse Reinforcement Learning](https://arxiv.org/abs/2001.09227) by Farzan Memarian, Ufuk Topcu + + +Issue# 13：2020-1-20 ---- 1. [Direct and indirect reinforcement learning](https://arxiv.org/abs/1912.10600) by Yang Guan, Bo Cheng 2. [Parameterized Indexed Value Function for Efficient Exploration in Reinforcement Learning](https://arxiv.org/abs/1912.10577) by Tian Tan, Vikranth R. Dwaracherla @@ -15,7 +36,7 @@ Issue# 11：2020-1-20 6. [Optimal Options for Multi-Task Reinforcement Learning Under Time Constraints](https://arxiv.org/abs/2001.01620) by Manuel Del Verme, Gianluca Baldassarre 7. [MushroomRL: Simplifying Reinforcement Learning Research](https://arxiv.org/abs/2001.01102) by Carlo D'Eramo, Jan Peters -Issue# 11：2020-1-10 +Issue# 12：2020-1-10 ---- 1. [Predictive Coding for Boosting Deep Reinforcement Learning with Sparse Rewards](https://arxiv.org/abs/1912.13414) by Xingyu Lu, Pieter Abbeel