Self-play and using an expert to learn to play backgammon with tem- poral difference learning. Journal of Intelligent Learning Systems and Applications, 2(2):57-68, 2010.M. Wiering, "Self-play and using an expert to learn to play backgammon with temporal difference learning," Journal of ...
Temporal Difference Learning of Backgammon Strategy This paper presents a case study in which the TD(位) algorithm for training connectionist networks, proposed in (Sutton, 1988), is applied to learning the game of backgammon from the outcome of self-play. This is apparently the first app... ...
For an agent learning to play backgammon or chess, the natural rewards for winning, losing, and drawing are + 1, -1, and 0, respectively. It is important to remember that rewards define the ultimate goal of the learning process. The rewards delivered to a reinforcement-learning agent should...
Perhaps the most successful application of TD(λ) is TD-Gammon, which was designed for networks to learn to play backgammon (Tesauro, 1995). Backgammon is an ancient two-player game that is played on an effectively one-dimensional track. The players take turns rolling dice and moving their ...
A class of connectionist networks is described that has learned to play backgammon at an intermediate-to-advanced level. The networks were trained by back-... G Tesauro,TJ Sejnowski - 《Artificial Intelligence》 被引量: 247发表: 1989年 Backpropagation and neurocontrol: a review and prospectus ...
to play the game of backgammon to a high standard (Tesauro, 1994). Here the network must learn to take a board position as input, along with the result of a dice throw, and produce a strong move as the output. This is done by having the network play against a copy of itself for ...
This method worked well on the backgammon game, but failed to generalize to new problems. More recently Riedmiller 2005 [4] used a system called neural-fitted Q learning, which used a multilayer perceptron to approximate the Q function. However, the approach had issues as it trained in batch...
We discuss some of the reasons for this success, principle among them being the use of on-line, rather than self-play. We also investigate whether TDLEAF(λ) can yield better results in the domain of backgammon, where TD(λ) has previously yielded striking success....
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Support learning made critical progress when a group of specialists utilized it to prepare a PC to play backgammon at a top-notch level. 2008: Google delivered the Google Forecast Programming interface, a cloud-based help that permitted designers to integrate AI into their applications. ...