Patent Document ID: 20100114807
Application ID: 12610709
Patent Flag: 0

Claim One:
1. A reinforcement learning system for making an agent learn an action policy for executing a task, comprising: an environment recognizing device configured to recognize a first to an nth (n≧2) state variables representing an environment; n numbers of jth (j=1, 2,. .. n) learning devices configured to calculate a jth reward based on a jth state variable recognized by the environment recognizing device, calculate a jth value according to a jth value function based on the jth state variable, calculate a jth error based on the jth reward and the jth value, and properly modify respectively the jth value function based on the jth error; and an action policy determining device configured to determine an action policy the agent should take based on a part or the entire part of the jth reward calculated by the n numbers of the jth learning device, wherein an (i+1)th (i=1, 2,. .. n−1) learning device is configured to calculate an (i+1)th reward based on a value of an ith reward function related to an ith state variable and a value of an ith value gradient function which is a temporal differential of an ith value function.