Benchmarking Real-Time Reinforcement Learning
Decision-making algorithms can require fast response time in applications as diverse as self-driving cars and minimizing load times of webpages. Yet, modern algorithms (deep reinforcement learning) are usually developed in scenarios where inference and training computational costs are ignored. This proposal aims to study reinforcement learning and control algorithms for real-time continuous control. In this scenario, the environment continuously evolves while actions are being computed by the agent (either in training or inference). The first goal is to provide a clear picture of the performance of modern algorithms modulated by their computational costs. The second goal is to identify the major challenges that arise when considering real-time environments to guide further research.