AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
A Bayesian Framework for Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Bayesian sparse sampling for on-line reward optimization
ICML '05 Proceedings of the 22nd international conference on Machine learning
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
General game learning using knowledge transfer
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Model based Bayesian exploration
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
An Algorithm for Transfer Learning in a Heterogeneous Environment
ECML PKDD '08 Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I
Transferring Instances for Model-Based Reinforcement Learning
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Learning to Recognize Activities from the Wrong View Point
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
A Bayesian sampling approach to exploration in reinforcement learning
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Improving reinforcement learning agents using genetic algorithms
AMT'10 Proceedings of the 6th international conference on Active media technology
Learning the behavior model of a robot
Autonomous Robots
Integrating reinforcement learning with human demonstrations of varying ability
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Multi-Task reinforcement learning: shaping and feature selection
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Bayesian multitask inverse reinforcement learning
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Transfer in reinforcement learning via shared features
The Journal of Machine Learning Research
Tree ensembles for predicting structured outputs
Pattern Recognition
Budgeted knowledge transfer for state-wise heterogeneous RL agents
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part I
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
Hi-index | 0.00 |
We consider the problem of multi-task reinforcement learning, where the agent needs to solve a sequence of Markov Decision Processes (MDPs) chosen randomly from a fixed but unknown distribution. We model the distribution over MDPs using a hierarchical Bayesian infinite mixture model. For each novel MDP, we use the previously learned distribution as an informed prior for modelbased Bayesian reinforcement learning. The hierarchical Bayesian framework provides a strong prior that allows us to rapidly infer the characteristics of new environments based on previous environments, while the use of a nonparametric model allows us to quickly adapt to environments we have not encountered before. In addition, the use of infinite mixtures allows for the model to automatically learn the number of underlying MDP components. We evaluate our approach and show that it leads to significant speedups in convergence to an optimal policy after observing only a small number of tasks.