COMPARATIVE ANALYSIS OF DEEP LEARNING ALGORITHMS WITH REINFORCEMENT DDPG, PPO AND SAC FOR UNMANNED CAR CONTROL IN CARLA SIMULATOR
This paper presents a comparative analysis of three advanced deep reinforcement learning algorithms: Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) implemented in the Stable Baselines 3 library. The aim of the study is to evaluate the performance and applicability of each of the algorithms for the task of driving an unmanned vehicle in the complex and dynamic environment provided by the CARLA simulator, focusing on key metrics such as total distance, total reward, average speed, deviation from the center of the roadway, and success rate of episodes. The authors describe the experimental testing methodology in detail, including the tuning of training parameters and performance evaluation criteria. Experimental results demonstrate differences in the performance of the algorithms, revealing their strengths and weaknesses in the context of autonomous driving. The paper contributes to the understanding of the advantages and limitations of each algorithm in the context of autonomous driving and offers recommendations for their practical application.
Tikhonov M.K. Comparative analysis of deep learning algorithms with reinforcement DDPG, PPO and SAC for unmanned car control in CARLA simulator // Research result. Information technologies. – Т.9, №2, 2024. – P. 69-74. DOI: 10.18413/2518-1092-2024-9-2-0-8
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