Automatic Abstraction in Reinforcement Learning Using Ant System Algorithm

Nowadays developing autonomous systems, which can act in various environments and interactively perform their assigned tasks, are intensively desirable. These systems would be ready to be applied in different fields such as medicine, controller robots and social life. Reinforcement learning is an attractive area of machine learning which addresses these concerns. In large scales, learning performance of an agent can be improved by using hierarchical Reinforcement Learning techniques and temporary extended actions. The higher level of abstraction helps the learning agent approach lifelong learning goals. In this paper a new method is presented for discovering subgoal states and constructing useful skills. The method utilizes Ant System optimization algorithm to identify bottleneck edges, which act like bridges between different connected areas of the problem space. Using discovered subgoals, the agent creates temporal abstractions, which enable it to explore more effectively. Experimental Results show that the proposed method can significantly improve the learning performance of the agent.