Machine Learning
Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Learning to Drive a Bicycle Using Reinforcement Learning and Shaping
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Autonomous shaping: knowledge transfer in reinforcement learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Coevolutionary temporal difference learning for Othello
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Formal analysis, hardness, and algorithms for extracting internal structure of test-based problems
Evolutionary Computation
| Hi-index | 0.00 |
To acquire expert skills in a sequential decision making domain that is too vast to be explored thoroughly, an intelligent agent has to be capable of inducing crucial knowledge from the most representative parts of it. One way to shape the learning process and guide the learner in the right direction is effective selection of such parts that provide the best training experience. To realize this concept, we propose a shaping method that orchestrates the training by iteratively exposing the learner to subproblems generated autonomously from the original problem. The main novelty of the proposed approach consists in equalling the learning process with the search in subproblem space and in employing a coevolutionary algorithm to perform this search. Each individual in the population encodes a sequence of subproblems that is evaluated by confronting the learner trained on it with other learners shaped in this way by particular individuals. When applied to the game of Othello, temporal difference learning on the best found subproblem sequence yields substantially better players than learning on the entire problem at once.