Technical Note: \cal Q-Learning
Machine Learning
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Machine Learning
Dynamic Programming
Linear Machine Decision Trees
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Reinforcement learning with selective perception and hidden state
Reinforcement learning with selective perception and hidden state
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Input generalization in delayed reinforcement learning: an algorithm and performance comparisons
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 2
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
The Lumberjack Algorithm for Learning Linked Decision Forests
SARA '02 Proceedings of the 4th International Symposium on Abstraction, Reformulation, and Approximation
TTree: Tree-Based State Generalization with Temporally Abstract Actions
Proceedings of the 5th International Symposium on Abstraction, Reformulation and Approximation
From Global Selective Perception to Local Selective Perception
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Dynamic programming for structured continuous Markov decision problems
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Interactive learning of mappings from visual percepts to actions
ICML '05 Proceedings of the 22nd international conference on Machine learning
A hybrid generative and predictive model of the motor cortex
Neural Networks
Learning the required number of agents for complex tasks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Resolution-Based Policy Search for Imperfect Information Differential Games
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Application of SONQL for real-time learning of robot behaviors
Robotics and Autonomous Systems
Non-parametric policy gradients: a unified treatment of propositional and relational domains
Proceedings of the 25th international conference on Machine learning
Simulation of sequential data: An enhanced reinforcement learning approach
Expert Systems with Applications: An International Journal
Reinforcement Learning with Orthonormal Basis Adaptation Based on Activity-Oriented Index Allocation
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Adaptive modeling and planning for reactive agents
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Closed-loop learning of visual control policies
Journal of Artificial Intelligence Research
IJCAI'99 Proceedings of the 16th international joint conference on Artifical intelligence - Volume 1
Fuzzy decision tree function approximation in reinforcement learning
International Journal of Artificial Intelligence and Soft Computing
The lumberjack algorithm for learning linked decision forests
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
TTree: tree-based state generalization with temporally abstract actions
Adaptive agents and multi-agent systems
Efficient vision-based navigation
Autonomous Robots
Task allocation learning in a multiagent environment: Application to the RoboCupRescue simulation
Multiagent and Grid Systems
Multivariate decision tree function approximation for reinforcement learning
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
BRA: An Algorithm for Simulating Bounded Rational Agents
Computational Economics
Induced states in a decision tree constructed by Q-learning
Information Sciences: an International Journal
Q-Tree: automatic construction of hierarchical state representation for reinforcement learning
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Policy sharing between multiple mobile robots using decision trees
Information Sciences: an International Journal
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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Reinforcement learning is an effective technique for learning action policies in discrete stochastic environments, but its efficiency can decay exponentially with the size of the state space. In many situations significant portions of a large state space may be irrelevant to a specific goal and can be aggregated into a few, relevant, states. The U Tree algorithm generates a tree based state discretization that efficiently finds the relevant state chunks of large propositional domains. In this paper, we extend the U Tree algorithm to challenging domains with a continuous state space for which there is no initial discretization. This Continuous U Tree algorithm transfers traditional regression tree techniques to reinforcement learning. We have performed experiments in a variety of domains that show that Continuous U Tree effectively handles large continuous state spaces. In this paper, we report on results in two domains, one gives a clear visualization of the algorithm and another empirically demonstrates an effective state discretization in a simple multi-agent environment.