CFP: PRICAI’18 Special Track on Reinforcement Learning
August 27, 2018, Nanjing, China
Scope and Background:
Reinforcement learning (RL) is an active field of research that deals with the problem of (single or multiple agents') sequential decision-making in unknown and possibly partially observable domains, whose dynamics may be deterministic, stochastic or adversarial. In the last few years, we have seen a growing interest in RL from both research communities and industries, and recent developments in exploration-exploitation, online learning, planning, and representation learning are making RL more and more appealing to real-world applications, with promising results in challenging domains such as recommendation systems, computer games, or robotic control.
This special track focuses on both theoretical models and algorithms of RL and its practical applications in various domains. The ultimate goal is to bring together diverse viewpoints in the RL area in an attempt to consolidate the common ground, identify new research directions, and promote the rapid advance of RL research community.
Topics:
The special track will cover a range of sub-topics in RL, from theoretical aspects to empirical evaluations, including but not limited to:
Exploration/exploitation
Deep RL, function approximation in RL
Policy search methods
Batch RL
Kernel methods for RL
Evolutionary RL
Partially observable RL, POMDP, predictive state representations
Bayesian RL
Multi-agent RL
RL in non-stationary domains
Life-long RL
Non-standard Criteria in RL, e.g., risk-sensitive RL, multi-objective RL, preference-based RL
Transfer Learning in RL
Model-based RL, simulation-based RL, planning-based RL
Knowledge representation in RL
Hierarchical RL
Interactive RL
Planning under uncertainty
RL in psychology and neuroscience
Applications of RL, e.g., in recommender systems, robotics, video games, finance, autonomous driving, healthcare.
Submission Guidelines:
All submission and publication guidelines announced for the PRICAI 2018 conference
(http://cse.seu.edu.cn/pricai18/) will be applicable for this special session. All papers should be submitted electronically using the conference management tool in PDF/DOC format and formatted using the
Springer LNAI template.
Submitted papers should not exceed 12 pages (excluding references), and must not be published or under consideration to be published elsewhere.
Paper Submission:
Papers submitted to the special track and the main conference will use the same submission system. Please choose “Reinforcement Learning” special track in the submission system
(https://easychair.org/conferences/?conf=pricai2018). The option is under "Additional submission choices" in the submission page.
Publication:
All papers submitted will be peer-reviewed using the same criteria of PRICAI-18. The accepted papers will be included in the conference proceedings of PRICAI-18, which will be published by Springer as a volume of
LNAI series. Selected papers will be considered to publish on SCI indexed journals, such as
Frontiers of Computer Science.
Important Dates:
* Full Paper Submission: March 31, 2018
* Notification of Acceptance: May 31, 2018
* Camera Ready Submission: June 11, 2018
* Main Conference: August 27-31, 2018
Track Chairs:
Chao Yu, Dalian University of Technology, China
Jianye Hao, Tianjin University, China
Yang Yu, Nanjing University, China
Zongzhang Zhang, Soochow University, China
Contact Person:
Dr. Chao Yu
Dalian University of Technology
Email: cy496@dlut.edu.cn
Dr. Jianye Hao
Tianjin University
Email: jianye.hao@tju.edu.cn