Papers › PGDQN: Preference-Guided Deep Q-Network

PGDQN: Preference-Guided Deep Q-Network

3 Oct 2023IEEE Transactions on Neural Networks and Learning Systems 2023 10archive 2025-07-28

Wenhui Huang, Cong Zhang, Jingda Wu, Xiangkun He, Jie Zhang, Chen Lv.

Stochastic exploration is the key to the success of the Deep Q-network (DQN) algorithm. However, most existing stochastic exploration approaches either explore actions heuristically regardless of their Q-values or couple the sampling with Q-values which inevitably introduce bias into the learning process. In this paper, we propose a novel preference-guided ϵ-greedy exploration algorithm that can efficiently facilitate exploration for DQN without introducing additional bias. Specifically, we design a dual architecture consisting of two branches, one of which is a copy of DQN, namely the Q-branch. The other branch, which we call the preference branch, learns the action preference that the DQN implicitly follows. We theoretically prove that the policy improvement theorem holds for the preference-guided ϵ-greedy policy and experimentally show that the inferred action preference distribution aligns with the landscape of corresponding Q-values. Intuitively, the preference-guided ϵ-greedy exploration motivates the DQN agent to take diverse actions so that actions with larger Q-values can be sampled more frequently, and those with smaller Q-values still have a chance to be explored, thus encouraging the exploration. We comprehensively evaluate the proposed method by benchmarking it with well-known DQN variants in nine different environments. Extensive results confirm the superiority of our proposed method in terms of performance and convergence speed. The demonstrative video and source code are available at \url{https://github.com/OscarHuangWind/Preference-Guided-DQN-Atari}.

Paper PDF Code

Code

OscarHuangWind/Preference-Guided-DQN-Atari officialmentioned in paperpytorch report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

Not run by Syntology. Nothing on this page verifies that the listed code works.

Tasks

Atari Games Benchmarking Efficient Exploration Q-Learning Reinforcement Learning (RL)

Results from the paper archive 2025-07-28

No leaderboard rows for this paper in the archive.

Methods

Convolution DQN Dense Connections Q-Learning

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections