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Temporal difference (TD) learning is a model-free reinforcement learning technique, which adopts an infinite horizon discount model and uses an incremental learning technique for dynamic programming. The state value function is updated in terms of sample episodes. Utilising eligibility traces is a key mechanism in enhancing the rate of convergence. TD(λ) represents the use of eligibility traces by introducing the parameter λ. However, the underlying mechanism of eligibility traces with andoi:10.1504/ijkesdp.2009.021982 fatcat:5o3yhthfrfbzhek5vvjaprgrvq