Two agents learn concurrently by interacting with each other without any need for simulated users (SUs) to train against or corpora to learn from.
we compare the Qlearning, Policy Hill-Climbing (PHC) and Win or Learn Fast Policy Hill-Climbing (PHC-WoLF) algorithms, varying the scenario complexity (state space size), the number of training episodes, the learning rate, and the exploration rate.