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Its name comes from the fact that it is an artificial neural net trained by a form of temporal-difference learning, specifically TD-Lambda. The final version of TD-Gammon (2.1) was trained with 1.5 million games of self-play, and achieved a level of play just slightly below that of the top human backgammon players of the time.
Temporal difference (TD) learning refers to a class of model-free reinforcement learning methods which learn by bootstrapping from the current estimate of the value function. These methods sample from the environment, like Monte Carlo methods , and perform updates based on current estimates, like dynamic programming methods.
State–action–reward–state–action (SARSA) is an algorithm for learning a Markov decision process policy, used in the reinforcement learning area of machine learning.It was proposed by Rummery and Niranjan in a technical note [1] with the name "Modified Connectionist Q-Learning" (MCQ-L).
Flash Element TD has not been updated since and still increases in popularity some two years on. [1] In December 2007, Scott and Paul Preece also created the Casual Collective Archived 2009-05-02 at the Wayback Machine , whose flagship game was a multiplayer version of Desktop Tower Defense . [ 3 ]
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Concept learning may be simple or complex because learning takes place over many areas. When a concept is difficult, it is less likely that the learner will be able to simplify, and therefore will be less likely to learn. Colloquially, the task is known as learning from examples.
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