# Conditional Generative Adversarial Network

New-media art generation faces costly user-preference collection and diffusion sampling without interpretable guidance. The CART-rule-guided diffusion model first uses a conditional GAN to expand 2,000 real ratings into 10,000 virtual agents and 500,000 preference records, then converts CART decision-tree rules into a differentiable gradient injected into the reverse sampling of Stable Diffusion 1.5. Against five baselines it reaches FID 29.56, preference accuracy 0.734 and Hit Rate@3 0.512, improving over the strongest baseline RF-AIGC by 13.8%, 9.4% and 19.6%.

New-media art generation faces costly user-preference collection and diffusion sampling without interpretable guidance. The CART-rule-guided diffusion model first uses a conditional GAN to expand 2,000 real ratings into 10,000 virtual agents and 500,000 preference records, then converts CART decision-tree rules into a differentiable gradient injected into the reverse sampling of Stable Diffusion 1.5. Against five baselines it reaches FID 29.56, preference accuracy 0.734 and Hit Rate@3 0.512, improving over the strongest baseline RF-AIGC by 13.8%, 9.4% and 19.6%.

Continuous-control reinforcement learning struggles with exploration and long-horizon credit assignment, and the usual fix, action noise or entropy regularization, explores without direction. GANGSTER uses a conditional generative adversarial network to propose candidate subgoals from the current state, scores them with structured losses for desirability, reachability and diversity, and feeds the chosen subgoal back only as intrinsic reward shaping, leaving the core TD3 and SAC update rules untouched. On MuJoCo's Ant-v5, GANGSTER with TD3 cuts time-to-solve by about 31% and raises final performance by 53% over baseline TD3; on HalfCheetah-v5 with SAC it raises final performance by 10% and lifts post-solution consistency above 99%.

Continuous-control reinforcement learning struggles with exploration and long-horizon credit assignment, and the usual fix, action noise or entropy regularization, explores without direction. GANGSTER uses a conditional generative adversarial network to propose candidate subgoals from the current state, scores them with structured losses for desirability, reachability and diversity, and feeds the chosen subgoal back only as intrinsic reward shaping, leaving the core TD3 and SAC update rules untouched. On MuJoCo's Ant-v5, GANGSTER with TD3 cuts time-to-solve by about 31% and raises final performance by 53% over baseline TD3; on HalfCheetah-v5 with SAC it raises final performance by 10% and lifts post-solution consistency above 99%.