Abstract
In traditional adaptive dynamic programming (ADP), only one step estimate is considered for training process, Thus, learning efficiency is lower. If more steps estimates are included, learning process will be speed up. Eligibility traces record the past and current gradients of estimation. It can be used to work with ADP for speeding up learning. In this paper, heuristic dynamic programming (HDP) which is a typical structure of ADP is considered. An algorithm, HDP(lambda), integrating HDP with eligibility traces is presented. The algorithm is illustrated from both forward view and back view for clear comprehension. Equivalency of two views is analyzed. Furthermore, differences between HDP and HDP(lambda) are considered from both aspects of theoretic analysis and simulation results. The problem of balancing a pendulum robot (pendubot) is adopted as a benchmark. The results indicate that compared to HDP, HDP(lambda) shows higher convergence rate and training efficiency.
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