ORGANIZED BYABDULRAHMAN ALTAHHAN VASILE PALADE, JUNYU DONG, XINGHUI DONG, HUI YU AND MOHAMED CHERIET
SCHOOL OF COMPUTING, ELECTRONICS AND MATHS, FACULTY OF ENGINEERING, COVENTRY UNIVERSITY
Deep Learning has been under the focus of neural network research and industrial communities due to its proven ability to scale well into difficult problems and due to its performance breakthroughs over other architectural and learning techniques in important benchmarking problems. This was mainly in the form of improved data representation in supervised learning tasks. Reinforcement learning (RL) is considered the model of choice for problems that involve learning from interaction, where the target is to optimize a long term control strategy or to learn to formulate an optimal policy. Typically these applications involve processing a stream of data coming from different sources, ranging from central massive databases to pervasive smart sensors.
RL does not lend itself naturally to deep learning and currently there is no uniformed approach to combine deep learning with reinforcement learning despite good attempts. Examples of important open questions are: How to make the state-action learning process deep? How to make the architecture of an RL system appropriate to deep learning without compromising the interactivity of the system? Etc. Although recently there have been important advances in dealing with these issues, they are still scattered and with no overarching framework that promote them in a well-defined and natural way.
This special session will provide a unique platform for researchers from Deep Learning and Reinforcement Learning communities to share their research experience towards a uniformed Deep Reinforcement Learning (DRL) framework in order to allow this important interdisciplinary branch to take-off on solid grounds. It will focus on the potential benefits of the different approaches to combine RL and DL. The aim is to bring more focus onto the potential of infusing reinforcement learning framework with deep learning capabilities that could allow it to deal more effectively with present applications including, but not restricted to, online streamed data processing that involves actions.
SCOPE AND TOPICS
- Novel DRL Algorithms
- Novel DRL Neural Architectures
- Adaptation of existing RL Techniques for Deep Learning
- Optimization and convergence proofs for DRL algorithms
- Deeply Hierarchical RL
- DRL architecture and algorithms for Control
- DRL architecture and algorithms for Robotics
- DRL architecture and algorithms for Time Series
- DRL architecture and algorithms for Big Streamed Data Processing
- DRL architecture and algorithms for Optimizing Governmental Policy
- Other DRL applications