# Soft Actor–Critic

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%.

Distribution-network self-healing has traded decision quality, real-time speed and explainability against one another: mathematical programming nears the optimum but is slow, while deep reinforcement learning decides in milliseconds but stays opaque. Embedding a graph attention network as the shared backbone of a soft actor–critic agent and writing a resilience index into the reward brings online decisions to 2.3 ms on the IEEE 33-bus and 123-bus systems, within 2.8% of the mixed-integer linear programming optimum. Attention heatmaps and SHAP attributions show where the agent looks and why it decides.

Distribution-network self-healing has traded decision quality, real-time speed and explainability against one another: mathematical programming nears the optimum but is slow, while deep reinforcement learning decides in milliseconds but stays opaque. Embedding a graph attention network as the shared backbone of a soft actor–critic agent and writing a resilience index into the reward brings online decisions to 2.3 ms on the IEEE 33-bus and 123-bus systems, within 2.8% of the mixed-integer linear programming optimum. Attention heatmaps and SHAP attributions show where the agent looks and why it decides.