Learning policies for partially observable environments: scaling up
Readings in agents
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Automatic Discovery of Subgoals in Reinforcement Learning using Diverse Density
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discovering Hierarchy in Reinforcement Learning with HEXQ
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Learning Options in Reinforcement Learning
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Autonomous discovery of temporal abstractions from interaction with an environment
Autonomous discovery of temporal abstractions from interaction with an environment
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Using relative novelty to identify useful temporal abstractions in reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Dynamic abstraction in reinforcement learning via clustering
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Markov Decision Processes in Artificial Intelligence
Markov Decision Processes in Artificial Intelligence
Reinforcement learning with perceptual aliasing: the perceptual distinctions approach
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Improving reinforcement learning by using sequence trees
Machine Learning
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Extended sequence tree is a direct method for automatic generation of useful abstractions in reinforcement learning, designed for problems that can be modelled as Markov decision process. This paper proposes a method to expand the extended sequence tree method over reinforcement learning to cover partial observability formalized via partially observable Markov decision process through belief state formalism. This expansion requires a reasonable approximation of information state. Inspired by statistical ranking, a simple but effective discretization schema over belief state space is defined. Extended sequence tree method is modified to make use of this schema under partial observability, and effectiveness of resulting algorithm is shown by experiments on some benchmark problems.