Challenges in training Deep Q Network

Deep Q Network (DQN) is a popular deep reinforcement learning algorithm that has shown remarkable performance in solving complex problems in various domains. However, DQN suffers from many challenges including the choice of appropriate hyperparameters, which is a crucial aspect of training DQN models. Many literatures provide information about the algorithm but not much is available which gives an insight into the hyperparameter tuning. The process of training deep RL algorithms often requires a significant amount of time for experimentation to tune the hyperparameters. Hence, numerical experimentation should be done judiciously. This paper provides insight for choosing DQN hyperparameters to improve its performance and reduce training time. We experimented with different hyperparameters such as network architecture, reward function, target update frequency, and exploration strategies to evaluate their impact on the algorithm’s performance. Our results show that the choice of hyperparameters significantly affects the performance of DQN, and selecting them appropriately can improve the model’s convergence rate and stability. Our approach can help researchers speed up the training process and provide a starting point for selecting hyperparameters while using DQN in real-world applications.