Reinforcement Learning for Adversarial Environments

Abstract

This paper explores the development of more intelligent and competitive AI agents for adversarial environments. A hide and seek simulation environment with three sensor models is developed, including a lidar, a far filed sensor, and a near-filed sensor. Four AI vs AI adversarial scenarios are investigated using the Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) reinforcement learning (RL) algorithms. Various experimental results across RL algorithms and sensor models have shown the seeker and hider have the most competitive advantage in the scenario of a SAC seeker versus a PPO hider and a PPO seeker versus a SAC hider, respectively. Additionally, the impact of sensing modalities on agent learning performance is investigated. Comparative studies reveal that extra sensing modalities improve agent performance, and the far-field sensor outperforms the near-field sensor. The results also suggest that an agent with a competitive advantage of AI algorithm is more resilient to variations in sensing modalities.