TD-Gammon, a self-teaching backgammon program, achieves master-level play
Neural Computation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Experimental Research in Evolutionary Computation: The New Experimentalism (Natural Computing Series)
Reinforcement learning with n-tuples on the game connect-4
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
| Hi-index | 0.00 |
We investigate reinforcement learning methods, namely the temporal difference learning TD(茂戮驴) algorithm, on game-learning tasks. Small modifications in algorithm setup and parameter choice can have significant impact on success or failure to learn. We demonstrate that small differences in input features influence significantly the learning process. By selecting the right feature set we found good results within only 1/100 of the learning steps reported in the literature. Different metrics for measuring success in a reproducible manner are developed. We discuss why linear output functions are often preferable compared to sigmoid output functions.