Evaluating the Effectiveness of Deep Reinforcement Learning Algorithms in a Walking Environment

Abstract

Deep Reinforcement Learning algorithms have shown to perform well on complex tasks, such as video games and chess.However, when it comes to locomotive tasks, picking the right algorithm and hyperparameters continues to be a challenge for many researchers.This project addressed that issue by determining which one of three reinforcement learning algorithms worked most effectively to help a computer learn to walk, without any external supervision or guidance, in a simulated environment.In addition, the project also determined the best learning rate for the algorithms by testing out 6 learning rates.A walking environment was used as it is considered to be a good representative for a large class of reinforcement learning problems.Proximal policy optimization was found to be the most effective, followed by the trust-region policy optimization and the vanilla policy gradient.The algorithms worked best with learning rate 1e-3.