The first Asian Workshop on Reinforcement Learning (AWRL 2016) focuses on both theoretical models and algorithms of reinforcement learning (RL) and its practical applications. In the last few years, we have seen the growing interest in RL of researchers from different research areas and industries. We invite reinforcement learning researchers and practitioners to participate in this world-class gathering. We intend to make this an exciting event for researchers and practitioners in RL worldwide, not only for the presentation of top quality papers, but also as a forum for the discussion of open problems, future research directions and application domains of RL. AWRL 2016 will consist of keynote talks (TBA), contributed paper presentations, discussion sessions spread over a one-day period.

Reinforcement learning (RL) is an active field of research that deals with the problem of (single or multiple agents') sequential decision-making in unknown possibly partially observable domains, whose (potentially non-stationary) dynamics may be deterministic, stochastic or adversarial. RL's objective is to develop agents' capability of learning optimal policies in unknown environments (possibly in face of other coexisting agents) by trial-and-error and with limited supervision. 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 robotics systems. We would like to create a forum to discuss interesting results both theoretically and empirically related with RL. The ultimate goal of this workshop 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.