Moving in a Simulated Environment Through Deep Reinforcement Learning

Abstract

Reinforcement learning is a field of artificial intelligence that is continuously evolving and has a wide variety of applications. In recent years major progress has been made in the application of deep reinforcement learning to high-dimensional problems with continuous state and action spaces. This paper presents a complete analysis of the application of the soft actor-critic algorithm to teach a four legged robot with three joints on each leg how to move towards the center of a virtually simulated environment. The general formulation of the reinforcement learning problem is first presented, followed by the description of the environment under analysis and the applied algorithm. Afterwards, the obtained results are compared against those of a manually programmed policy, closing with a discussion of some key design choices and common challenges.