Dennis Konkol
Benchmarking of Deep Reinforcement Learning Algorithms on Embedded GPUs

Abstract
This bachelor’s thesis on Deep Reinforcement Learning (DRL) algorithms compares multiple models on various embedded GPUs. The focus is on evaluating DRL algorithms’ performance and power consumption, particularly DQN and DDPG. Base implementations in Python (stablebaselines3), available as open source, serve as the starting point. Two specific implementations are considered: one for engineered motor control from the University of Paderborn and a freely chosen application from the OpenAI Gym environment. Here, I chose the CartPole environment which will be discussed later in this thesis. Furthermore, a custom DQN implementation for CUDA GPUs is developed, benchmarked, and compared to the Python base implementation.