## Archive for the ‘Reinforcement Learning’ Category

### Arxiv on Feb. 16th

16Feb18

Title: Reinforcement Learning from Imperfect Demonstrations Authors: Yang Gao, Huazhe (Harry) Xu, Ji Lin, Fisher Yu, Sergey Levine, Trevor Darrell Categories: cs.AI cs.LG stat.ML Robust real-world learning should benefit from both demonstrations and interactions with the environment. Current approaches to learning from demonstration and reward perform supervised learning on expert demonstration data and use reinforcement […]

### Arxiv on Feb. 15th

15Feb18

No paper today in the digest about Deep Reinforcement Learning.

### Arxiv on Feb. 14th

15Feb18

Title: Efficient Exploration through Bayesian Deep Q-Networks Authors: Kamyar Azizzadenesheli and Emma Brunskill and Animashree Anandkumar Categories: cs.AI cs.LG stat.ML We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration in high dimensions through posterior sampling but is usually computationally expensive. We address this […]

### Arxiv on Feb. 13th

13Feb18

Title: More Robust Doubly Robust Off-policy Evaluation Authors: Mehrdad Farajtabar, Yinlam Chow, and Mohammad Ghavamzadeh Categories: cs.AI We study the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of a policy from the data generated by another policy(ies). In particular, we focus on the doubly robust (DR) […]

### Arxiv on Feb. 12th

12Feb18

Title: A Unified Approach for Multi-step Temporal-Difference Learning with   Eligibility Traces in Reinforcement Learning Authors: Long Yang, Minhao Shi, Qian Zheng, Wenjia Meng, Gang Pan Categories: cs.AI cs.LG stat.ML Recently, a new multi-step temporal learning algorithm, called $Q(\sigma)$, unifies $n$-step Tree-Backup (when $\sigma=0$) and $n$-step Sarsa (when $\sigma=1$) by introducing a sampling parameter $\sigma$. However, similar […]

### Arxiv on Feb. 9th

09Feb18

Title: Deep Reinforcement Learning for Image Hashing Authors: Jian Zhang, Yuxin Peng and Zhaoda Ye Categories: cs.CV Comments: 18 pages, submitted to ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). arXiv admin note: text overlap with arXiv:1612.02541 Deep hashing methods have received much attention recently, which achieve promising results by taking advantage of the […]

### Arxiv on Feb. 8th

08Feb18

Title: A Critical Investigation of Deep Reinforcement Learning for Navigation Authors: Vikas Dhiman, Shurjo Banerjee, Brent Griffin, Jeffrey M Siskind, Jason J Corso Categories: cs.RO cs.AI The navigation problem is classically approached in two steps: an exploration step, where map-information about the environment is gathered; and an exploitation step, where this information is used to […]

### Arxiv on Feb. 7th

07Feb18

Title: Shared Autonomy via Deep Reinforcement Learning Authors: Siddharth Reddy, Sergey Levine, Anca Dragan Categories: cs.LG cs.HC cs.RO In shared autonomy, user input is combined with semi-autonomous control to achieve a common goal. The goal is often unknown ex-ante, so prior work enables agents to infer the goal from user input and assist with the task. Such methods […]

### Arxiv on Feb. 5th

05Feb18

Well, I will cheat a little for today since there is no paper related to Reinforcement Learning on Arxiv. As a consequence, I browse the one from Feb. 2nd… Title: Elements of Effective Deep Reinforcement Learning towards Tactical   Driving Decision Making Authors: Jingchu Liu, Pengfei Hou, Lisen Mu, Yinan Yu, Chang Huang Categories: cs.AI cs.LG Comments: […]