Performance Analysis of Reinforcement Learning Techniques for Augmented Experience Training Using Generative Adversarial Networks
Smita Rajendra Mahajan, Shruti Patil, Moinuddin Bhavnagri, Rashmi Singh, Kshitiz Kalra, Bhumika Saini, Ketan V. Kotecha, Jatinderkumar R. Saini · Applied Sciences · 2022
This paper aims at analyzing the performance of reinforcement learning (RL) agents when trained in environments created by a generative adversarial network (GAN). This is a first step towards the greater goal of developing fast-learning and robust RL agents by leveraging the power of GANs for environment generation. The RL techniques that we tested were exact Q-learning, approximate Q-learning, approximate SARSA and a heuristic agent. The task for the agents was to learn how to play the game Super Mario Bros (SMB). This analysis will be helpful in suggesting which RL techniques are best suited for augmented experience training (with synthetic environments). This would further help in establishing a reinforcement learning framework using the agents that can learn faster by bringing a greater variety in environment exploration.