Using combination of actions in reinforcement learning

Abstract

Software agents are programs that can observe their environment and act in an attempt to reach their design goals. In most cases the selection of particular agent architecture determines the behaviour in response to the different problem states However, there are some problem domains in which it is desirable that the agent learns a good action execution policy by interacting with its environment. This kind of learning is called Reinforcement Learning and it is useful in the process control area. Given a problem state, the agent selects the adequate action to do and receives an immediate reward, then estimations about every action are updated and, after a certain period of time, the agent learns which the best action to be executed is. Most reinforcement learning algorithms perform simple actions while two or more are capable of being used. This work involves the use of RL algorithms to find an optimal policy in a gridworld problem and proposes a mechanism to combine actions of different types.