Reinforcement Learning for Real-Time Optimization in NB-IoT Networks

Reinforcement Learning in Neural Networks: A Survey

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

Reinforcement Learning in Neural Networks: A Survey

In recent years, researches on reinforcement learning (RL) have focused on bridging the gap between adaptive optimal control and bio-inspired learning techniques. Neural network reinforcement learning (NNRL) is among the most popular algorithms in the RL framework. The advantage of using neural networks enables the RL to search for optimal policies more efficiently in several real-life applicat...

Real-Time Interactive Reinforcement Learning for Robots

It is our goal to understand the role real-time human interaction can play in machine learning algorithms for robots. In this paper we present Interactive Reinforcement Learning (IRL) as a plausible approach for training human-centric assistive robots by natural interaction. We describe an experimental platform to study IRL, pose questions arising from IRL, and discuss initial observations obta...

Real-Time Reinforcement Learning in Continuous Domains

Real-Time Reinforcement Learning in Continuous Domains