Title: Qualitative Measurements of Policy Discrepancy for Return-based Deep Q-Network,
Abstract: The deep Q-network (DQN) and return-based reinforcement learning are two
promising algorithms proposed in recent years. DQN brings advances to complex
sequential decision problems, while return-based algorithms have advantages in
making use of sample trajectories. In this paper, we propose a general
framework to combine DQN and most of the return-based reinforcement learning
algorithms, named R-DQN. We show the performance of traditional DQN can be
improved effectively by introducing return-based reinforcement learning. In
order to further improve the R-DQN, we design a strategy with two measurements
which can qualitatively measure the policy discrepancy. Moreover, we give the
two measurements' bounds in the proposed R-DQN framework. We show that
algorithms with our strategy can accurately express the trace coefficient and
achieve a better approximation to return. The experiments, conducted on several
representative tasks from the OpenAI Gym library, validate the effectiveness of
the proposed measurements. The results also show that the algorithms with our
strategy outperform the state-of-the-art methods.