The reward function design for the pong game based on reinforcement learning

Reinforcement learning (RL) has significant potential for game AI training and intelligent decision-making. However, designing effective reward functions remains challenging, designing effective reward functions for games remains challenging due to complexity in aligning desired behaviors and avoiding unintended outcomes. To address this, our experiment optimized a reward function utilizing the Proximal Policy Optimization (PPO) algorithm. The refined reward function integrated several key components designed to enhance agent performance and learning efficiency. This paper trained the RL agent using this customized reward structure and compared it against a model trained with the original reward function. Experimental results indicated that the optimized reward function substantially improved the agent’s game performance. Specifically, the customized approach enabled the agent to demonstrate enhanced strategic adaptability, accuracy, and efficiency. These findings highlight the importance and effectiveness of carefully constructed reward functions in improving the capabilities and outcomes of reinforcement learning agents.