We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...
This paper investigates a relatively new direction in Multiagent Reinforcement Learning. Most multiagent learning techniques focus on Nash equilibria as elements of both the learn...
We present experiences made with a course in applied computer science which was based on the concept of communities of practice. Within the scope of the course “Entrepreneurship...
Ralf Klamma, Matthias Jarke, Markus Rohde, Volker ...
- This paper follows the progress of improving the Arab Open University's Learning Management System by integrating it with other online systems, such as the university's...
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
Satinder P. Singh, Tommi Jaakkola, Michael I. Jord...