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
A Bayesian Approach for Learning and Planning in Partially Observable Markov Decision Processes
The Journal of Machine Learning Research
The Effect of Robust Decisions on the Cost of Uncertainty in Military Airlift Operations
ACM Transactions on Modeling and Computer Simulation (TOMACS)
Brief announcement: achieving reliability in master-worker computing via evolutionary dynamics
PODC '12 Proceedings of the 2012 ACM symposium on Principles of distributed computing
ℓ1-Penalized projected bellman residual
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
MapReduce for parallel reinforcement learning
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
An adaptive dialogue system with online dialogue policy learning
SETN'12 Proceedings of the 7th Hellenic conference on Artificial Intelligence: theories and applications
A comparative study of reinforcement learning techniques on dialogue management
EACL '12 Proceedings of the Student Research Workshop at the 13th Conference of the European Chapter of the Association for Computational Linguistics
Achieving reliability in master-worker computing via evolutionary dynamics
Euro-Par'12 Proceedings of the 18th international conference on Parallel Processing
APRIL: active preference learning-based reinforcement learning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Autonomous data-driven decision-making in smart electricity markets
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part II
Sparse gradient-based direct policy search
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Smart exploration in reinforcement learning using absolute temporal difference errors
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Machine learning for interactive systems and robots: a brief introduction
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
The Journal of Machine Learning Research
Linear fitted-Q iteration with multiple reward functions
The Journal of Machine Learning Research
Scenario Trees and Policy Selection for Multistage Stochastic Programming Using Machine Learning
INFORMS Journal on Computing
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
Scalable and efficient bayes-adaptive reinforcement learning based on monte-carlo tree search
Journal of Artificial Intelligence Research
Policy oscillation is overshooting
Neural Networks
A tour of machine learning: An AI perspective
AI Communications - ECAI 2012 Turing and Anniversary Track
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Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective.What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner's predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms' merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming.We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations.