Matrix analysis
The complexity of dynamic programming
Journal of Complexity
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Near-Optimal Reinforcement Learning in Polynomial Time
Machine Learning
R-max - a general polynomial time algorithm for near-optimal reinforcement learning
The Journal of Machine Learning Research
Least-squares policy iteration
The Journal of Machine Learning Research
Exploration and apprenticeship learning in reinforcement learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
An analytic solution to discrete Bayesian reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Reinforcement learning with limited reinforcement: using Bayes risk for active learning in POMDPs
Proceedings of the 25th international conference on Machine learning
Knows what it knows: a framework for self-aware learning
Proceedings of the 25th international conference on Machine learning
Cabernet: vehicular content delivery using WiFi
Proceedings of the 14th ACM international conference on Mobile computing and networking
Efficient reinforcement learning with relocatable action models
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Towards faster planning with continuous resources in stochastic domains
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Using linear programming for Bayesian exploration in Markov decision processes
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Model-based exploration in continuous state spaces
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
A unifying framework for computational reinforcement learning theory
A unifying framework for computational reinforcement learning theory
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
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To quickly achieve good performance, reinforcement-learning algorithms for acting in large continuous-valued domains must use a representation that is both sufficiently powerful to capture important domain characteristics, and yet simultaneously allows generalization, or sharing, among experiences. Our algorithm balances this tradeoff by using a stochastic, switching, parametric dynamics representation. We argue that this model characterizes a number of significant, real-world domains, such as robot navigati on across varying terrain. We prove that this representational assumption allows our algorithm to be probably approximately correct with a sample complexity that scales polynomially with all problem-specific quantities including the state-space dimension. We also explicitly incorporate the error introduced by approximate planning in our sample complexity bounds, in contrast to prior Probably Approximately Correct (PAC) Markov Decision Processes (MDP) approaches, which typically assume the estimated MDP can be solved exactly. Our experimental results on constructing plans for driving to work using real car trajectory data, as well as a small robot experiment on navigating varying terrain, demonstrate that our dynamics representation enables us to capture real-world dynamics in a sufficient manner to produce good performance.