Reinforcement Learning: An Introduction
@article{Sutton2005ReinforcementLA,
title={Reinforcement Learning: An Introduction},
author={R. Sutton and A. Barto},
journal={IEEE Transactions on Neural Networks},
year={2005},
volume={16},
pages={285-286}
}Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. [...] Key Method Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view…Expand Abstract
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