DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm
DoMo-AC is a novel algorithm combining multi-step policy evaluation and improvement to enhance off-policy learning. It addresses the difficulty of applying multi-step control methods in reinforcement learning by introducing a bias-variance trade-off via a trace coefficient. This method guarantees faster convergence to the optimal policy in general off-policy settings.