Motivated Optimal Developmental Learning for Sequential Tasks Without Using Rigid Time-Discounts.

Many methods for reinforcement learning use symbolic representations-nonemergent-such as Q-learning. We use emergent representations here, without human handcrafted symbolic states (i.e., each state corresponds to a different location). This paper models reinforcement learning for hidden neurons in emergent networks for sequential tasks. In this paper, their influences on sequential tasks (e.g., r...