This paper discusses a system that accelerates reinforcement learning by using transfer from related tasks. Without such transfer, even if two tasks are very similar at some abstract level, an extensive re-learning e ort is required. The system achieves much of its power by transferring parts of previously learned solutions rather than a single complete solution. The system exploits strong features in the multi-dimensional function produced by reinforcement learning in solving a particular task. These features are stable and easy to recognize early in the learning process. They generate a partitioning of the state space and thus the function. The partition is represented as a graph. This is used to index and compose functions stored in a case base to form a close approximation to the solution of the new task. Experiments demonstrate that function composition often produces more than an order of magnitude increase in learning rate compared to a basic reinforcement learning algorithm.
The application of Transfer Learning within Reinforcement Learning tasks was first proposed in REF , where an algorithm was defined that exploits strong features obtained from RL on one task in order to compose functions in a case base that is used on the solution of a new task.
Accelerating Reinforcement Learning by Composing Solutions of Automatically Identified Subtasks
Journal Of Artificial Intelligence Research, Volume 16, pages 59-104, 2002
Computer Science