Increasing the flexibility and speed of convergence of a learning agent

A review of the basic methods used to model a learning agent, such as instance-based learning, artificial neural networks and reinforcement learning, suggests that they either lack flexibility (can only be used to solve a small number of problems) or they tend to converge very slowly to the optimal policy. This paper describes and illustrates a set of processes that address these two shortcomings. The resulting learning agent is able to "adapt fairly well" to a much larger set of environments and is capable of doing this in a reasonable amount of time. In order to address the lack of flexibility and slow convergence to the optimal policy, the new learning agent becomes a hybrid between a learning agent based on instance-based learning and one based on reinforcement learning. To accelerate its convergence to its optimal policy, this new learning agent incorporates the use of a new concept we call propagation of good findings. Furthermore, to make a better use of the learning agent's memory resources,, and therefore increase its flexibility, we make use of another new concept we call moving prototypes.