… Reinforcement learning The remaining question is how the above components can be
used … We adopt a reinforcement learning (RL) approach to learn a policy resulting a highly …

… reinforcement learning approaches in social robotics. In addition to a survey, we categorize
existent reinforcement learning approaches … : interactive reinforcement learning, intrinsically …

… We apply reinforcement learning methods to learn domain-speci c heuristics for job shop
scheduling. A repair-based scheduler starts with a critical-path schedule and incrementally …

… the current success of deep learning and reinforcement learning to process control problems.
… The controller setup follows the typical reinforcement learning setup, whereby an agent (…

In this paper we introduce Ant-Q, a family of algorithms which present many similarities with
Q-learning (Watkins, 1989), and which we apply to the solution of symmetric and asymmetric …

… Our approach has been designed in such a way that … the operation of our reinforcement
learning techniques. The … Our reinforcement learning approach and its evaluation provides …

… There is an emerging trend of using deep reinforcement learning technique for traffic light …
In this paper, we propose a more effective deep reinforcement learning model for traffic light …

Reinforcement Learning (RL) provides a promising new approach to systems performance
management that differs radically from standard queuing-theoretic approaches making use of …

… In this paper, we propose a reinforcement learning approach for autonomic configuration
and … The RL approach is enhanced with an efficient initialization policy to reduce the learning …

… This paper describes the Q-routing algorithm for packet routing, in which a reinforcement
learning module is embedded into each node of a switching network. Only local communication …