Temporal difference reinforcement learning algorithms are perfectly suited to autonomous agents because they learn directly from an agent’s experience based on sequential actio...
This paper is concerned with on-line problems where a mobile robot of size D has to achieve a task in an unknown planar environment whose geometry is acquired by the robot during ...
We present here Extended Markov Tracking (EMT), a computationally tractable method for the online estimation of Markovian system dynamics, along with experimental support for its ...
We describe a hybrid linear programming (LP) and evolutionary algorithm (EA) based resource matcher suitable for heterogeneous grid environments. The hybrid matcher adopts the ite...
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...