Call for Papers: Special Issue on Deep Reinforcement Learning in Neural Networks https://www.journals.elsevier.com/neural-networks/call-for-papers/special-issue-on-deep-reinforcement-learning-in-neural-netwo
Deep learning (DL) has become highly popular in recent years, among theoretically minded and application-focused researchers alike. Moreover, the idea of deep learning has been combined with reinforcement learning (RL), leading to deep reinforcement learning, which has achieved notable successes in tackling difficult problems, including the achievement of AlphaGo. However, there are many open questions and issues that need to be addressed with regard to deep RL. Open questions with regard to deep RL include: · How do we extend RL algorithms or systems to make them suitable for deep learning? How do we make RL (typically centered on values of states or state-action pairings) appropriately deep? · How do we do so without jeopardizing useful characteristics of RL? · What modification and enhancements to learning algorithms are necessary to accomplish deep RL in an effective and/or efficient manner? · How can we make knowledge within deep RL systems explicit (generating explicit, symbolic, usable knowledge) and enable metacognitive reflection and regulation to some extent? · How can deep learning help facilitate planning or model-based reinforcement learning? · How can hierarchical or modular approaches be applied to deep RL? · What theoretical/mathematical properties can be obtained with regard to deep RL (e.g., convergence, stability, robustness, and optimality)? · How do we apply deep RL in real-world scenarios? The aim of this special issue is to showcase state-of-the-art work in the field of deep RL, addressing some of the above questions and beyond. Although there have no doubt been advances in addressing these questions, there is clearly room for further development. This special issue will provide a platform for deep learning and reinforcement learning researchers to share their work, for the sake of more rapid advances on a solid footing, fully realizing the potential of infusing reinforcement learning and deep learning. It also intends to showcase more effective applications in a variety of fields (robotics, control engineering, data analysis, and so on). We invite original research contributions on deep reinforcement learning (broadly defined). Possible topics for this special issue include, among others: · New and better deep RL algorithms · New and better neural network architectures for deep RL · Better combinations of existing algorithms and techniques for deep RL · Theories regarding deep RL · Transfer learning and prior knowledge within deep RL · Coping with uncertainty in deep RL · Combining policy learning, value learning, and model-based search · Symbolic structures from or within deep RL · Planning and deep RL · Mathematical analysis of deep RL (regarding convergence, optimality, stability, robustness, and so on) · Hierarchical or modular RL · Multi-agent RL · Applications of deep RL algorithms, architectures, and systems to robotics, control, data analysis, prediction and forecast, modeling and simulation, and so on · Applications of deep RL to cognitive-psychological or social modeling and analysis Survey papers are welcome also. Submission Procedure: Prospective authors should follow the standard author instructions for Neural Networks, and submit manuscripts online at http://ees.elsevier.com/neunet/. During submission, authors should indicate that their papers are for the special issue. Important Dates • July 1, 2017 – Deadline for submission • December 1, 2017 – Notification of review decisions to authors • February 1, 2018 – Deadline for submission of revised versions • April 1, 2018 – Final acceptance decision Guest Editors: Ron Sun, David Silver, Gerald Tesauro, Guang-Bin Huang ======================================================== Professor Ron Sun, Ph.D., FIEEE, FAPS, FPsyS Cognitive Science Department Rensselaer Polytechnic Institute 110 Eighth Street, Carnegie 302A Troy, NY 12180, USA phone: 518-276-3409 fax: 518-276-3017 email: dr.ron.sun [AT] gmail.com web: http://sites.google.com/site/drronsun ======================================================= _______________________________________________ uai mailing list uai@ENGR.ORST.EDU https://secure.engr.oregonstate.edu/mailman/listinfo/uai
