Before revision:  We conclude that reinforcement learning networks can efficiently learn optimal behavior in conditions when reward values are modulated by external motivational processes with arbitrary dynamics. Motivational salience can be viewed as a general-purpose model-free method identifying and capturing changes in subjective or objective values of multiple rewards. Networks with motivationmay also be parts of a larger hierarchical reinforcement learning system in the brain.
After revision:  We conclude that networks with motivation can rapidly adapt their behavior to varying conditions without changes in synaptic strength when expected reward is modulated by motivation. Such networks may also provide a mechanism for how hierarchical reinforcement learning system in the brain.