TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Infinite-horizon policy-gradient estimation
Journal of Artificial Intelligence Research
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Neurocomputing
Job control in heterogeneous computing systems
Journal of Computer and Systems Sciences International
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These lecture notes are intended to give a tutorial introduction to the formulation and analysis of reinforcement learning problems. In these problems, an agent chooses actions to take in some environment, aiming to maximize a reward function. Many control, scheduling, planning and game-playing tasks can be formulated in this way, as problems of controlling a Markov decision process. We review the classical dynamic programming approaches to finding optimal controllers. For large state spaces, these techniques are impractical. We review methods based on approximate value functions, estimated via simulation. In particular, we discuss the motivation for (and shortcomings of) the TD (λ) algorithm.