Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Elevator Group Control Using Multiple Reinforcement Learning Agents
Machine Learning
Gradient descent for general reinforcement learning
Proceedings of the 1998 conference on Advances in neural information processing systems II
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolving neural networks through augmenting topologies
Evolutionary Computation
The Vision of Autonomic Computing
Computer
Least-squares policy iteration
The Journal of Machine Learning Research
Utility Functions in Autonomic Systems
ICAC '04 Proceedings of the First International Conference on Autonomic Computing
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Machine learning for fast quadrupedal locomotion
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
ECML'05 Proceedings of the 16th European conference on Machine Learning
Empirical Studies in Action Selection with Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Reinforcement learning problems are commonly tackled with temporal difference methods, which attempt to estimate the agent's optimal value function. In most real-world problems, learning this value function requires a function approximator, which maps state-action pairs to values via a concise, parameterized function. In practice, the success of function approximators depends on the ability of the human designer to select an appropriate representation for the value function. A recently developed approach called evolutionary function approximation uses evolutionary computation to automate the search for effective representations. While this approach can substantially improve the performance of TD methods, it requires many sample episodes to do so. We present an enhancement to evolutionary function approximation that makes it much more sample-efficient by exploiting the off-policy nature of certain TD methods. Empirical results in a server job scheduling domain demonstrate that the enhanced method can learn better policies than evolution or TD methods alone and can do so in many fewer episodes than standard evolutionary function approximation.