This paper presents a novel incremental algorithm that combines Q-learning, a well-known dynamic programming-based reinforcement learning method, with the TD(A) return estimation process, which is typically used in actor-critic learning, another well-known dynamic programming-based reinforcement learning method. The parameter A is used to distribute credit throughout sequences of actions, leading to faster learning and also helping to alleviate the non-Markovian effect of coarse state-space quantization. The resulting algorithm, Q(λ)-learning, thus combines some of the best features of the Q-learning and actor-critic learning paradigms. The behavior of this algorithm is demonstrated through computer simulations of the standard benchmark control problem of learning to balance a pole on a cart.