Deep Q-network in the Iterated Prisoner's Dilemma under Noise

In game theory, there is a fundamental challenge about maintaining cooperation among selfish players, especially under practical noise. This study applies a noisy Iterated Prisoner’s Dilemma (IPD) model to investigate how learning strategies perform against classical strategies when players may receive false or misleading signals due to random observation errors. More specifically, this study compares Deep Q-Network (DQN) agents with basic Q-learning (QL) and several classical strategies such as Tit-for-Tat, Win-Stay-Lose-Shift, and Grudger. The experiment results show that when noise emerges, DQN agents not only achieve higher cumulative rewards than other strategies but also maintain more stability, adaptability, and resilience across repeated interactions. DQN agents’ deep neural structure helps them to capture long-term temporal dependencies, effectively differentiate accidental defections from intentional ones, and recover cooperation after disturbances by noise. These findings indicate that deep reinforcement learning is effective in noisy and imperfect settings. The findings also offers valuable insights for understanding the emergence of cooperation and for designing robust multi-agent decision-making mechanisms in noisy or uncertain environments.