An objective of reinforcement learning is to allow an agent to maximize reward and to minimize punishment. The above two cases are equal to each other and are finally recognized as one. Accordingly, learning in a reinforcing learning is driven according to a single reinforcement signal representing the reward and the punishment by a single scale of a value. The present invention expresses reward and punishment as two separated categories and includes two opposite expressions. Accordingly, the present invention suggests four elementary value signals representing evidence for reward, evidence against reward, evidence for punishment, and evidence against punishment. The present invention describes how to combine the four elementary value signals by various combinations to generate various effective reinforcement signals. A brain uses a method of learning total eight types of numeric expressions.
REINFORCEMENT LEARNING METHOD AND APPARATUS WITH ELEMENTARY VALUE SIGNALS