REINFORCEMENT LEARNING FOR INTELLIGENT GAMEPLAY

This project investigates the application of Reinforcement Learning (RL) to develop an intelligent gameplay system for the classic Snake game using a Deep Q-Network (DQN) algorithm. The study focuses on training an autonomous agent to navigate the game environment, make optimal decisions, and maximize cumulative rewards through trial-and-error learning. Key techniques such as experience replay and target network stabilization were implemented to enhance learning stability and efficiency. The agent was trained over 10,000 episodes, with performance evaluated using metrics such as average score and learning curves. Results indicate significant improvement in the agent’s decision-making capability, demonstrating the effectiveness of DQN in sequential decision-making tasks. This research contributes to the field of AI-driven gaming by showcasing the potential of reinforcement learning to enhance gameplay adaptability, optimize performance, and provide a foundation for more complex gaming applications.