An Upper Bound on the Loss from Approximate Optimal-Value Functions
Machine Learning
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
An algebraic approach to abstraction in reinforcement learning
An algebraic approach to abstraction in reinforcement learning
Transfer via inter-task mappings in policy search reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Autonomous transfer for reinforcement learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Value-function-based transfer for reinforcement learning using structure mapping
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Using Homomorphisms to transfer options across continuous reinforcement learning domains
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Decision tree methods for finding reusable MDP homomorphisms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Value functions for RL-based behavior transfer: a comparative study
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Building portable options: skill transfer in reinforcement learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Abstraction and generalization in reinforcement learning: a summary and framework
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Lossy stochastic game abstraction with bounds
Proceedings of the 13th ACM Conference on Electronic Commerce
Automatic construction of temporally extended actions for MDPs using bisimulation metrics
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Multi-Task reinforcement learning: shaping and feature selection
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Apprenticeship learning with few examples
Neurocomputing
Learning potential functions and their representations for multi-task reinforcement learning
Autonomous Agents and Multi-Agent Systems
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The field of transfer learning aims to speed up learning across multiple related tasks by transferring knowledge between source and target tasks. Past work has shown that when the tasks are specified as Markov Decision Processes (MDPs), a function that maps states in the target task to similar states in the source task can be used to transfer many types of knowledge. Current approaches for autonomously learning such functions are inefficient or require domain knowledge and lack theoretical guarantees of performance. We devise a novel approach that learns a stochastic mapping between tasks. Using this mapping, we present two algorithms for autonomous transfer learning -- one that has strong convergence guarantees and another approximate method that learns online from experience. Extending existing work on MDP homomorphisms, we present theoretical guarantees for the quality of a transferred value function.