Author name cluster

Yanna Wang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

1 author row

EAAI Journal 2026 Journal Article

A reinforcement learning-assisted differential evolution with population feature replay

Zijian Cao
Chuhang Qiao
Yanna Wang
Baolong Su
Zhihao Zhang
Feng Tian

As an effective global optimization method, differential evolution (DE) often faces limitations in search capability due to its differential mutation strategy and control parameters. To effectively address diverse problems, adaptively adjusting the control parameter and mutation strategy has become an important research direction. Motivated by this challenge, a reinforcement learning-assisted differential evolution with population feature replay (RLDE-PFR) is proposed. In RLDE-PFR, DE can autonomously adjust mutation strategies and control parameters based on the state of the population. In addition, the proposed population feature replay mechanism (PFR) fully exploits historical successful information to guide parameter generation. To comprehensively validate the performance of RLDE-PFR, we compared it with six state-of-the-art algorithms. The experiments were conducted on the CEC2015 and CEC2017 test suites and a practical Transformer hyperparameter optimization problem. The effectiveness of both reinforcement learning-based autonomous adjustment and the PFR is also validated. The experimental results indicate that RLDE-PFR exhibits competitive and superior performance in terms of resulting accuracy and search efficiency. The RLDE-PFR code is available at https: //github. com/Strive-code/rl-pfr. git.

Details DOI

AAAI Conference 2023 Conference Paper

PiCor: Multi-Task Deep Reinforcement Learning with Policy Correction

Fengshuo Bai
Hongming Zhang
Tianyang Tao
Zhiheng Wu
Yanna Wang
Bo Xu

Multi-task deep reinforcement learning (DRL) ambitiously aims to train a general agent that masters multiple tasks simultaneously. However, varying learning speeds of different tasks compounding with negative gradients interference makes policy learning inefficient. In this work, we propose PiCor, an efficient multi-task DRL framework that splits learning into policy optimization and policy correction phases. The policy optimization phase improves the policy by any DRL algothrim on the sampled single task without considering other tasks. The policy correction phase first constructs an adaptive adjusted performance constraint set. Then the intermediate policy learned by the first phase is constrained to the set, which controls the negative interference and balances the learning speeds across tasks. Empirically, we demonstrate that PiCor outperforms previous methods and significantly improves sample efficiency on simulated robotic manipulation and continuous control tasks. We additionally show that adaptive weight adjusting can further improve data efficiency and performance.

PDF Details DOI