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Sutton's nomination as a AAAI fellow reads: [12] For significant contributions to many topics in machine learning, including reinforcement learning, temporal difference techniques, and neural networks. In 2016, Sutton was elected Fellow of the Royal Society of Canada. [15] In 2021, he was elected Fellow of the Royal Society. [16]
During this time at UMass, Barto co-directed the Autonomous Learning Laboratory (initially the Adaptive Network Laboratory), which generated several key ideas in reinforcement learning. Richard Sutton , with whom he co-authored the influential book Reinforcement Learning: An Introduction (MIT Press 1998; 2nd edition 2018), was his first PhD ...
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.
The problem became more widely studied when Sutton and Barto added it to their book Reinforcement Learning: An Introduction (1998). [3] Throughout the years many versions of the problem have been used, such as those which modify the reward function , termination condition, and the start state .
PAC model-free reinforcement learning; Reinforcement Learning: An Introduction by Richard Sutton and Andrew S. Barto, an online textbook. See "6.5 Q-Learning: Off-Policy TD Control". Piqle: a Generic Java Platform for Reinforcement Learning; Reinforcement Learning Maze, a demonstration of guiding an ant through a maze using Q-learning
Intrinsic motivation is often studied in the framework of computational reinforcement learning [9] [10] (introduced by Sutton and Barto), where the rewards that drive agent behaviour are intrinsically derived rather than externally imposed and must be learnt from the environment. [11]
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Reinforcement learning (RL) is an interdisciplinary area of machine learning and optimal control concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised ...