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Guangfu Hao

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AAAI Conference 2025 Conference Paper

Efficient Reinforcement Learning Through Adaptively Pretrained Visual Encoder

  • Yuhan Zhang
  • Guoqing Ma
  • Guangfu Hao
  • Liangxuan Guo
  • Yang Chen
  • Shan Yu

While Reinforcement Learning (RL) agents can successfully learn to handle complex tasks, effectively generalizing acquired skills to unfamiliar settings remains a challenge. One of the reasons behind this is the visual encoder used are task-dependent, preventing effective feature extraction in different settings. To address this issue, recent studies have tried to pretrain encoders with diverse visual inputs in order to improve their performance. However, they rely on existing pretrained encoders without further exploring the impact of pretraining period. In this work, we propose APE: efficient reinforcement learning through Adaptively Pretrained visual Encoder—a framework that utilizes adaptive augmentation strategy during the pretraining phase and extracts useful features with only a few interactions within the task environments in the policy learning period. Experiments are conducted across various domains, including DeepMind Control Suite, Atari Games and Memory Maze benchmarks, to verify the effectiveness of our method. Results show that mainstream RL methods, such as DreamerV3 and DrQ-v2, achieve state-of-the-art performance when equipped with APE. In addition, APE significantly improves the sampling efficiency during learning, approaching the efficiency of state-based method using only visual inputs in several control tasks. These findings demonstrate the potential of adaptive pretraining of encoder in enhancing the generalization ability and efficiency of visual RL algorithms.

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AAMAS Conference 2025 Conference Paper

Mitigating Non-Stationarity in Deep Reinforcement Learning with Clustering Orthogonal Weight Modification

  • Guoqing Ma
  • Yuhan Zhang
  • Yuming Dai
  • Guangfu Hao
  • Yang Chen
  • Shan Yu

RL agents often operate under the assumption of environmental stationarity, which poses a great challenge to learning efficiency since many environments are inherently non-stationary in state distribution. To address this issue, we introduce the Clustering Orthogonal Weight Modified (COWM) layer, which can be integrated into the policy network of any RL algorithm and mitigate non-stationarity effectively. By employing clustering techniques and a projection matrix, the COWM layer stabilize the learning process. Empirically, the COWM layer is integrated into various RL methods and outperforms state-of-the-art methods on the DMControl benchmark, highlighting its robustness and generality across various tasks and algorithms.

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