We consider the policy search approach to reinforcement learning. We show that if a “baseline distribution” is given (indicating roughly how often we expect a good policy to v...
J. Andrew Bagnell, Sham Kakade, Andrew Y. Ng, Jeff...
As multiagent environments become more prevalent we need to understand how this changes the agent-based paradigm. One aspect that is heavily affected by the presence of multiple a...
This paper summarizes recent advances in the application of multiagent coordination algorithms to air traffic flow management. Indeed, air traffic flow management is one of the fu...
Reinforcement Learning is commonly used for learning tasks in robotics, however, traditional algorithms can take very long training times. Reward shaping has been recently used to ...
Ana C. Tenorio-Gonzalez, Eduardo F. Morales, Luis ...
In many multi agent learning problems, it is difficult to determine, a priori, the agent reward structure that will lead to good performance. This problem is particularly pronoun...