Temporal difference (TD) algorithms are attractive for reinforcement learning due to their ease-of-implementation and use of "bootstrapped" return estimates to make effi...
The existing reinforcement learning approaches have been suffering from the policy alternation of others in multiagent dynamic environments. A typical example is a case of RoboCup...
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
This paper introduces the RL-TOPs architecture for robot learning, a hybrid system combining teleo-reactive planning and reinforcement learning techniques. The aim of this system ...
This paper describes the use of machine learning to improve the performance of natural language question answering systems. We present a model for improving story comprehension th...