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There was great interest andĮxcitement in the 1980s regarding this topic, motivated most The second line of research addressed the training ofĪrtificial neural networks as nonlinear function approximatorsįor classification and regression. TD(\(\lambda\)) learning algorithm (Sutton, 1988) used in TD-Gammon. "temporal difference learning" and invented the The most notableĬontribution was by Rich Sutton, who coined the term Through subsequent research on reinforcement learning (RL)Īnd temporal difference learning. Samuel's work was provided a much stronger mathematical foundation With the work of Arthur Samuel on checkers. Made during a self-play game so as to improve the Temporal credit assignment problem, i.e.,Īpportioning credit or blame to each of the moves The first aimed at developing self-play learning algorithms TD-Gammon built upon three different historic lines of research. Level of play in backgammon tournaments in recent years.
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The new knowledge generated by these neural nets was widelyĭisseminated, resulting in enormous improvements in the Jellyfish and Snowie, directly inspired by TD-Gammon. The commercial release of two world-class neural net programs, This revolution was further accelerated with Revolution in concepts and strategies used by human expertīackgammon players. Innovative style of play and rollout analyses sparked a Management, and call admission and routing in Production scheduling, financial trading systems and portfolio Learning in domains such as elevator dispatch, job-shop scheduling,Ĭell-phone channel assignment, assembly line optimization and Inspired numerous subsequent applications of reinforcement Machine learning and computer game-playing research, and TD-Gammon was regarded as a breakthrough achievement in Its positional judgement rivaled that of human experts, and whenĬombined with shallow lookahead, it reached a level of play that Methodology produced a surprisingly strong program: without lookahead, Starting from random initial play, TD-Gammon's self-teaching To estimate the expected outcome of a given backgammon position. In order to train the weights of a multi-layer perceptron With gradient-descent, i.e., error back-propagation, Specifically, temporal difference learning was combined with
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TD-Gammon combined neural networks with reinforcement learning. To teach itself to play backgammon solely by playingĪgainst itself and learning from the results. In the early 1990s by IBM researcher Gerald Tesauro that was able TD-Gammon is a machine learning program developed Performance is measured by expected points per game (ppg) won or lost against a benchmark opponent (Sun Microsystems' Gammontool program). Figure 1: A sample learning curve of one of the original nets of (Tesauro, 1992), containing 10 hidden units, showing playing strength as a function of the number of self-play training games.