soft-actor-critic

This paper proposes a novel architecture integrating multi-head attention with the Soft Actor-Critic algorithm for porosity prediction and process parameter optimization in additive manufacturing, achieving faster convergence and higher rewards than standard RL methods.

This paper introduces C-DSAC, a new distributional reinforcement learning algorithm that uses the Cramér distance to improve performance and stability in robotic benchmarks compared to standard SAC.