Related Experiment Videos
Using the XCS classifier system for multi-objective reinforcement learning problems
1Faculty of Computing, Engineering and Mathematics, University of West of England, Bristol BS16 1QY, UK. matthew2.studley@uwe.ac.uk
Abstract:
We investigate the performance of a learning classifier system in some simple multi-objective, multi-step maze problems, using both random and biased action-selection policies for exploration. Results show that the choice of action-selection policy can significantly affect the performance of the system in such environments. Further, this effect is directly related to population size, and we relate this finding to recent theoretical studies of learning classifier systems in single-step problems.
Related Concept Videos
Multi-input and Multi-variable systems
In the absence of...
Classification of Systems-I
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II
Reinforcement Schedules
Once a behavior is learned,...
Collisions in Multiple Dimensions: Problem Solving
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Reinforcement
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example: