A New Programming Interface for Reinforcement Learning Simulations

摘要

The field of Reinforcement Learning, a sub-field of machine learning, represents an important direction for research in Artificial Intelligence, the way for improving an agent's behavior, given a certain feed-back about its performance. In this paper we propose an original interface for programming reinforcement learning simulations in known environments. Using this interface, there are possible simulations both for reinforcement learning based on the states' utilities and learning based on actions' values (Q-learning).