Evaluating Deep Reinforcement Learning Algorithms

Recent advancements in machine learning, partic- ularly in reinforcement learning (RL), have enabled solutions to previously intractable problems. This research paper delves into the mathematical underpinnings of several prominent deep RL algorithms, including REINFORCE, A2C, DDPG, and SAC. By implementing and testing these algorithms in the MuJoCo simulator, I evaluate their performance in training agents to achieve complex tasks, such as walking in a 3D environment. Our findings demonstrate the efficacy of these algorithms in real-time learning and adaptation, underscored by the superior performance of the SAC model. This study not only provides insights into the practical applications of deep RL but also lays the groundwork for future explorations in hyperparameter optimization and multi-agent learning scenarios. The project repository containing code, models, and experimental results is available for further research and development.