The computation of a rigid body transformation which optimally aligns a set of measurement points with a surface and related registration problems are studied from the viewpoint o...
Helmut Pottmann, Qi-Xing Huang, Yong-Liang Yang, S...
Although tabular reinforcement learning (RL) methods have been proved to converge to an optimal policy, the combination of particular conventional reinforcement learning techniques...
Multi-agent games are becoming an increasingly prevalent formalism for the study of electronic commerceand auctions. The speed at which transactions can take place and the growing...
Satinder P. Singh, Michael J. Kearns, Yishay Manso...
Stochastic games are a generalization of MDPs to multiple agents, and can be used as a framework for investigating multiagent learning. Hu and Wellman (1998) recently proposed a m...
We consider approximate policy evaluation for finite state and action Markov decision processes (MDP) in the off-policy learning context and with the simulation-based least square...