Technical Note: \cal Q-Learning
Machine Learning
Robot shaping: developing autonomous agents through learning
Artificial Intelligence
Reinforcement learning with replacing eligibility traces
Machine Learning - Special issue on reinforcement learning
Creating advice-taking reinforcement learners
Machine Learning - Special issue on reinforcement learning
Machine Learning - Special issue on inductive transfer
Stochastic dynamic programming with factored representations
Artificial Intelligence
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Structure in the Space of Value Functions
Machine Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Reinforcement Learning and Shaping: Encouraging Intended Behaviors
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An introduction to variable and feature selection
The Journal of Machine Learning Research
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Automatic shaping and decomposition of reward functions
Proceedings of the 24th international conference on Machine learning
Multi-task reinforcement learning: a hierarchical Bayesian approach
Proceedings of the 24th international conference on Machine learning
Transfer Learning via Inter-Task Mappings for Temporal Difference Learning
The Journal of Machine Learning Research
Transfer via inter-task mappings in policy search reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Transfer of samples in batch reinforcement learning
Proceedings of the 25th international conference on Machine learning
Proceedings of the 25th international conference on Machine learning
Transfer of task representation in reinforcement learning using policy-based proto-value functions
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Social reward shaping in the prisoner's dilemma
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Transfer in variable-reward hierarchical reinforcement learning
Machine Learning
Convex multi-task feature learning
Machine Learning
Co-evolution of Shaping Rewards and Meta-Parameters in Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Regularization and feature selection in least-squares temporal difference learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Transfer via soft homomorphisms
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Improving action selection in MDP's via knowledge transfer
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Efficient structure learning in factored-state MDPs
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Potential-based shaping in model-based reinforcement learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
A model of inductive bias learning
Journal of Artificial Intelligence Research
Potential-based shaping and Q-value initialization are equivalent
Journal of Artificial Intelligence Research
Training and tracking in robotics
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
State abstraction discovery from irrelevant state variables
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Automatic Feature Selection for Model-Based Reinforcement Learning in Factored MDPs
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Theoretical and Empirical Analysis of Reward Shaping in Reinforcement Learning
ICMLA '09 Proceedings of the 2009 International Conference on Machine Learning and Applications
Transfer Learning for Reinforcement Learning Domains: A Survey
The Journal of Machine Learning Research
Multi-task evolutionary shaping without pre-specified representations
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A complexity analysis of cooperative mechanisms in reinforcement learning
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 2
Time-based reward shaping in real-time strategy games
ADMI'10 Proceedings of the 6th international conference on Agents and data mining interaction
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Structural knowledge transfer by spatial abstraction for reinforcement learning agents
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Extraction of reward-related feature space using correlation-based and reward-based learning methods
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Darwinian embodied evolution of the learning ability for survival
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Theoretical considerations of potential-based reward shaping for multi-agent systems
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Multi-agent, reward shaping for RoboCup KeepAway
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Policy invariance under reward transformations for general-sum stochastic games
Journal of Artificial Intelligence Research
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
Multi-Task reinforcement learning: shaping and feature selection
EWRL'11 Proceedings of the 9th European conference on Recent Advances in Reinforcement Learning
Dynamic potential-based reward shaping
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Transfer in reinforcement learning via shared features
The Journal of Machine Learning Research
Modeling difference rewards for multiagent learning
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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In multi-task learning, there are roughly two approaches to discovering representations. The first is to discover task relevant representations, i.e., those that compactly represent solutions to particular tasks. The second is to discover domain relevant representations, i.e., those that compactly represent knowledge that remains invariant across many tasks. In this article, we propose a new approach to multi-task learning that captures domain-relevant knowledge by learning potential-based shaping functions, which augment a task's reward function with artificial rewards. We address two key issues that arise when deriving potential functions. The first is what kind of target function the potential function should approximate; we propose three such targets and show empirically that which one is best depends critically on the domain and learning parameters. The second issue is the representation for the potential function. This article introduces the notion of $$k$$k-relevance, the expected relevance of a representation on a sample sequence of $$k$$k tasks, and argues that this is a unifying definition of relevance of which both task and domain relevance are special cases. We prove formally that, under certain assumptions, $$k$$k-relevance converges monotonically to a fixed point as $$k$$k increases, and use this property to derive Feature Selection Through Extrapolation ofk-relevance (FS-TEK), a novel feature-selection algorithm. We demonstrate empirically the benefit of FS-TEK on artificial domains.