Technical Note: \cal Q-Learning
Machine Learning
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Temporal difference learning and TD-Gammon
Communications of the ACM
Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
Programming backgammon using self-teaching neural nets
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Games solved: now and in the future
Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Machine learning in games: a survey
Machines that learn to play games
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Sequential cost-sensitive decision making with reinforcement learning
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Programming
What a Neural Network Can Learn About Othello
What a Neural Network Can Learn About Othello
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Simulation-Based Optimization: Parametric Optimization Techniques and Reinforcement Learning
Nash q-learning for general-sum stochastic games
The Journal of Machine Learning Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Observing the evolution of neural networks learning to play the game of Othello
IEEE Transactions on Evolutionary Computation
Value-function reinforcement learning in Markov games
Cognitive Systems Research
A Q-learning approach to derive optimal consumption and investment strategies
IEEE Transactions on Neural Networks
Coevolution in a large search space using resource-limited nash memory
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Training neural networks to play backgammon variants using reinforcement learning
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Adaptive exploration using stochastic neurons
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Gradient algorithms for exploration/exploitation trade-offs: global and local variants
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
An evolutionary-based hyper-heuristic approach for the Jawbreaker puzzle
Applied Intelligence
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Operations research and management science are often confronted with sequential decision making problems with large state spaces. Standard methods that are used for solving such complex problems are associated with some difficulties. As we discuss in this article, these methods are plagued by the so-called curse of dimensionality and the curse of modelling. In this article, we discuss reinforcement learning, a machine learning technique for solving sequential decision making problems with large state spaces. We describe how reinforcement learning can be combined with a function approximation method to avoid both the curse of dimensionality and the curse of modelling. To illustrate the usefulness of this approach, we apply it to a problem with a huge state space-learning to play the game of Othello. We describe experiments in which reinforcement learning agents learn to play the game of Othello without the use of any knowledge provided by human experts. It turns out that the reinforcement learning agents learn to play the game of Othello better than players that use basic strategies.