Advancing motivated learning with goal creation

摘要

This paper reports improvements to our Motivated Learning (ML) model. These include modifications to the calculation of need/pain biases, pain-goal weights, and how actions are selected. Resource based abstract pains are complemented with pains related to desired and undesired actions by other agents. Probability based selection of goals is discussed. The minimum amount of desired resources is now set automatically by the agent. Additionally, we have presented several comparisons of Motivated Learning performance against some well-known reinforcement learning algorithms.