 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.
<clarity>  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.
 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.