-dependant setup times, etc. A multi-step reinforcement learning (RL) algorithm called Sarsa([lambda], k) is proposed and applied to …

This paper investigates the impact of reward shaping in multi-agent reinforcement learning as a way to incorporate …

The design of reinforcement learning solutions to many problems artificially constrain the action set available to an … solution quality and the sample complexity improve significantly when basic reinforcement learning is coupled with action … action discovery to multi-agent reinforcement learning. We show that Joint Action Learners (JALs) indeed learn coordination …

. The results illustrate that simple reinforcement learning is sufficient to detect correlation between investments. …

This paper examines the convergence of payoffs and strategies in Erev and Roth`s model of reinforcement learning. When …

processes by applying sophisticated machine learning techniques such as Relational Reinforcement Learning and Particle Swarm …