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TD-Lambda is a learning algorithm invented by Richard S. Sutton based on earlier work on temporal difference learning by Arthur Samuel. [11] This algorithm was famously applied by Gerald Tesauro to create TD-Gammon , a program that learned to play the game of backgammon at the level of expert human players.
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.
Tower defense is seen as a subgenre of real-time strategy video games, due to its real-time origins, [2] [3] even though many modern tower defense games include aspects of turn-based strategy. Strategic choice and positioning of defensive elements is an essential strategy of the genre.
The stacked layers of learning are called an autocurriculum. Autocurricula are especially apparent in adversarial settings, [29] where each group of agents is racing to counter the current strategy of the opposing group. The Hide and Seek game is an accessible example of an autocurriculum occurring in an adversarial setting. In this experiment ...
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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).
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Human feedback is commonly collected by prompting humans to rank instances of the agent's behavior. [15] [17] [18] These rankings can then be used to score outputs, for example, using the Elo rating system, which is an algorithm for calculating the relative skill levels of players in a game based only on the outcome of each game. [3]