Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems

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

Reinforcement Learning In Real-Time Strategy Games

We consider the problem of effective and automated decisionmaking in modern real-time strategy (RTS) games through the use of reinforcement learning techniques. RTS games constitute environments with large, high-dimensional and continuous state and action spaces with temporally-extended actions. To operate under such environments we propose Exlos, a stable, model-based MonteCarlo method. Contra...

Complexity Analysis of Real-Time Reinforcement Learning

Real-Time Reinforcement Learning Sven Koenig and Reid G. Simmons School of Computer Science Carnegie Mellon University Pittsburgh, PA 15213-3891 [email protected], [email protected] Abstract This paper analyzes the complexity of on-line reinforcement learning algorithms, namely asynchronous realtime versions of Q-learning and value-iteration, applied to the problem of reaching a goal state in d...