Mind over machine: the power of human intuition and expertise in the era of the computer
Mind over machine: the power of human intuition and expertise in the era of the computer
Practical Issues in Temporal Difference Learning
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
Explanation-Based Neural Network Learning: A Lifelong Learning Approach
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Relational Reinforcement Learning
Machine Learning
Learning Logical Definitions from Relations
Machine Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Bounding the Suboptimality of Reusing Subproblem
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
State abstraction for programmable reinforcement learning agents
Eighteenth national conference on Artificial intelligence
Reusing Old Policies to Accelerate Learning on New MDPs TITLE2:
Reusing Old Policies to Accelerate Learning on New MDPs TITLE2:
Using Options for Knowledge Transfer in Reinforcement Learning TITLE2:
Using Options for Knowledge Transfer in Reinforcement Learning TITLE2:
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Autonomous learning of sequential tasks: experiments and analyses
IEEE Transactions on Neural Networks
Probabilistic policy reuse in a reinforcement learning agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Cross-domain transfer for reinforcement learning
Proceedings of the 24th international conference on Machine learning
Transfer Learning in Reinforcement Learning Problems Through Partial Policy Recycling
ECML '07 Proceedings of the 18th European conference on Machine Learning
Integrated cognitive architectures: a survey
Artificial Intelligence Review
Experiments with Adaptive Transfer Rate in Reinforcement Learning
Knowledge Acquisition: Approaches, Algorithms and Applications
Cross-domain knowledge transfer using structured representations
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Learning relational options for inductive transfer in relational reinforcement learning
ILP'07 Proceedings of the 17th international conference on Inductive logic programming
Using spatial hints to improve policy reuse in a reinforcement learning agent
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Policy transfer via Markov logic networks
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Stochastic abstract policies for knowledge transfer in robotic navigation tasks
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Transferring evolved reservoir features in reinforcement learning tasks
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Transfer learning in multi-agent reinforcement learning domains
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
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This paper describes an extension to reinforcement learning (RL), in which a standard RL algorithm is augmented with a mechanism for transferring experience gained in one problem to new but related problems. In this approach, named Progressive RL, an agent acquires experience of operating in a simple environment through experimentation, and then engages in a period of introspection, during which it rationalises the experience gained and formulates symbolic knowledge describing how to behave in that simple environment. When subsequently experimenting in a more complex but related environment, it is guided by this knowledge until it gains direct experience. A test domain with 15 maze environments, arranged in order of difficulty, is described. A range of experiments in this domain are presented, that demonstrate the benefit of Progressive RL relative to a basic RL approach in which each puzzle is solved from scratch. The experiments also analyse the knowledge formed during introspection, illustrate how domain knowledge may be incorporated, and show that Progressive Reinforcement Learning may be used to solve complex puzzles more quickly.