G
Gerald Tesauro
Researcher at IBM
Publications - 142
Citations - 12519
Gerald Tesauro is an academic researcher from IBM. The author has contributed to research in topics: Reinforcement learning & Artificial neural network. The author has an hindex of 52, co-authored 138 publications receiving 11420 citations. Previous affiliations of Gerald Tesauro include University of Illinois at Urbana–Champaign.
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Journal ArticleDOI
Temporal difference learning and TD-Gammon
TL;DR: The domain of complex board games such as Go, chess, checkers, Othello, and backgammon has been widely regarded as an ideal testing ground for exploring a variety of concepts and approaches in artificial intelligence and machine learning.
Journal ArticleDOI
TD-Gammon, a self-teaching backgammon program, achieves master-level play
TL;DR: The latest version of TD-Gammon is now estimated to play at a strong master level that is extremely close to the world's best human players.
Journal ArticleDOI
Practical Issues in Temporal Difference Learning
TL;DR: It is found that, with zero knowledge built in, the network is able to learn from scratch to play the entire game at a fairly strong intermediate level of performance, which is clearly better than conventional commercial programs, and which surpasses comparable networks trained on a massive human expert data set.
Journal ArticleDOI
Temporal Difference Learning and TD-Gammon
TL;DR: TD-GAMMON is a neural network that trains itself to be an evaluation function for the game of backgammon by playing against itself and learning from the outcome.
Proceedings ArticleDOI
Utility functions in autonomic systems
TL;DR: A distributed architecture, implemented in a realistic prototype data center, that demonstrates how utility functions can enable a collection of autonomic elements to continually optimize the use of computational resources in a dynamic, heterogeneous environment is presented.