A critical state identification approach to inverse reinforcement learning for autonomous systems.

Inverse reinforcement learning features a reward function based on reward features and positive demonstrations. When complex learning tasks are performed, the entire state space is used to form the set of reward features, but this large set results in a long computational time. Retrieving important states from the full state space addresses this problem. This study formulated a method that extracts critical features by combining negative and positive demonstrations of searching for critical states from the entire state space to increase learning efficiency. In this method, two types of demonstrations are used: positive demonstrations, which are given by experts and agents imitate, and negative demonstrations, which demonstrate incorrect motions to be avoided by agents. All significant features are extracted by identifying the critical states over the entire state space. This is achieved by comparing the difference between the negative and positive demonstrations. When these critical states are identified, they form the set of reward features, and a reward function is derived that enables agents to learn a policy using reinforcement learning quickly. A speeding car simulation was used to verify the proposed method. The simulation results demonstrate that the proposed approach allows an agent to search for a positive strategy and that the agent then displays intelligent expert-like behavior. [ABSTRACT FROM AUTHOR]