Optimizing reinforcement learning in complex environments using neural networks

This paper presents the distinct mechanisms and applications of traditional Q-learning (QL) and Deep Q-learning (DQL) within the realm of reinforcement learning (RL). Traditional Q-learning (QL) utilizes the Bellman equation to update Q-values stored in a Q-table, making it suitable for simple environments. However, its scalability is limited due to the exponential growth of state-action pairs in complex environments. Deep Q-learning (DQL) addresses this limitation by using neural networks to approximate Q-values, thus eliminating the need for a Q-table, and enabling efficient handling of complex environments. The neural network (NN), acting as the agent's decision-making brain, learns to predict Q-values through training, adjusting its weights based on received rewards. The study highlights the importance of well-calibrated reward systems in reinforcement learning (RL). Proper reward structures guide the agent towards desired behaviors while minimizing unintended actions. By running multiple environments simultaneously, the training process is accelerated, allowing the agent to gather diverse experiences and improve its performance efficiently. Comparative analysis of training models demonstrates that a well-balanced reward system results in more consistent and effective learning. The findings underscore the necessity of careful design in reinforcement learning systems to ensure optimal agent behavior and efficient learning outcomes in both simple and complex environments. Through this research, we gain valuable insights into the application of Q-learning (QL) and Deep Q-learning (DQL), enhancing our understanding of how agents learn and adapt to their environments.