Self Organizing Classifiers: First Steps in Structured Evolutionary Machine Learning
This work addresses incremental improvements in structured evolutionary machine learning for reinforcement learning tasks, focusing on more difficult problems than existing state-of-the-art continuous input-action multi-step ones.
The authors tackled the challenge of applying evolutionary machine learning to complex, dynamic continuous input-action mazes by proposing a variation of self-organizing classifiers using a parameterless self-organizing map and a genetic operator, achieving good performance in big, noisy, and dynamically changing environments.
Learning classifier systems (LCSs) are evolutionary machine learning algorithms, flexible enough to be applied to reinforcement, supervised and unsupervised learning problems with good performance. Recently, self organizing classifiers were proposed which are similar to LCSs but have the advantage that in its structured population no balance between niching and fitness pressure is necessary. However, more tests and analysis are required to verify its benefits. Here, a variation of the first algorithm is proposed which uses a parameterless self organizing map (SOM). This algorithm is applied in challenging problems such as big, noisy as well as dynamically changing continuous input-action mazes (growing and compressing mazes are included) with good performance. Moreover, a genetic operator is proposed which utilizes the topological information of the SOM's population structure, improving the results. Thus, the first steps in structured evolutionary machine learning are shown, nonetheless, the problems faced are more difficult than the state-of-art continuous input-action multi-step ones.