Reward Functions for Accelerated Learning
Reinforcement learning (RL) has become the methodology of choice for learning in a variety of different domains.
Delayed reinforcement is related to the way goals are formulated in a learning system. One of the main properties of situated domains is that agents pursues multiple goals, some of which are maintained continuously, while others are achieved and terminated.
intermittent reinforcement can be used similarly, by weighting the reward according to the accomplished progress. Intermediate reinforcement can be introduced in two ways: 1) by reinforcing multiple goals, and 2) by using progress estimators.
Progress estimators are partial internal critics. They are associated with specific goals and, when active, provide a metric of improvement relative to those goals.
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