VA-learning as a more efficient alternative to Q-learning
VA-learning is a value-based reinforcement learning algorithm that learns both a value function (V) and an advantage function (A) directly, instead of relying solely on Q-functions. It decomposes the Q-function into V(x) + A(x, a), allowing it to improve sample efficiency over traditional Q-learning in both tabular and deep RL settings.