Opposition-based Q(λ) algorithm

The problem of delayed reward in reinforcement learning is usually tackled by implementing the mechanism of eligibility traces. In this paper we introduce an extension of eligibility traces to solve one of the challenging problems in reinforcement learning. The concept of opposition traces is proposed in this work to deal with large state space problems in reinforcement learning applications. We combine the idea of opposition and eligibility traces to construct the oppositionbased Q(λ). The results are compared with the conventional Watkins' Q(λ) and reflect a remarkable performance increase.