Controlled Use of Subgoals in Reinforcement Learning

Abstract

Reinforcement learning A learning agent observes the state of its environment, chooses an action based on its current policy and executes the action. Responding to the action, the environment transitions to a new state, and a reword is given to the agent when applicable. The reward indicates how good or how bad the new state is, and the agent uses it to improve its policy so that it can obtain more rewards. Since reinforcement learning (abbreviated as RL hereafter) requires no other information, e.g. a model of environment, than the perceived states and rewards, it can be applied to a class of problems where the environment is complex or uncertain. The applications of RL include control of multi-legged robots (Kimura et al.,