Academic

WINFlowNets: Warm-up Integrated Networks Training of Generative Flow Networks for Robotics and Machine Fault Adaptation

arXiv:2603.17301v1 Announce Type: new Abstract: Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retrieval network. Despite their demonstrated efficiency compared to state-of-the-art Reinforcement Learning (RL) algorithms, their practical application in robotic control tasks is constrained by the reliance on pre-training the retrieval network. This dependency poses challenges in dynamic robotic environments, where pre-training data may not be readily available or representative of the current environment. This paper introduces WINFlowNets, a novel CFlowNets framework that enables the co-training of flow and retrieval networks. WINFlowNets begins with a warm-up phase for the retrieval network to bootstrap its policy, followed by a shared training architecture and a shared replay buffer for co-training both networks. Experiments in simulated robotic environments

Z
Zahin Sufiyan, Shadan Golestan, Yoshihiro Mitsuka, Shotaro Miwa, Osmar Zaiane · · 1 min read · 7 views
#cs.LG

arXiv:2603.17301v1 Announce Type: new Abstract: Generative Flow Networks for continuous scenarios (CFlowNets) have shown promise in solving sequential decision-making tasks by learning stochastic policies using a flow and a retrieval network. Despite their demonstrated efficiency compared to state-of-the-art Reinforcement Learning (RL) algorithms, their practical application in robotic control tasks is constrained by the reliance on pre-training the retrieval network. This dependency poses challenges in dynamic robotic environments, where pre-training data may not be readily available or representative of the current environment. This paper introduces WINFlowNets, a novel CFlowNets framework that enables the co-training of flow and retrieval networks. WINFlowNets begins with a warm-up phase for the retrieval network to bootstrap its policy, followed by a shared training architecture and a shared replay buffer for co-training both networks. Experiments in simulated robotic environments demonstrate that WINFlowNets surpasses CFlowNets and state-of-the-art RL algorithms in terms of average reward and training stability. Furthermore, WINFlowNets exhibits strong adaptive capability in fault environments, making it suitable for tasks that demand quick adaptation with limited sample data. These findings highlight WINFlowNets' potential for deployment in dynamic and malfunction-prone robotic systems, where traditional pre-training or sample inefficient data collection may be impractical.

Executive Summary

WINFlowNets, a novel Generative Flow Networks framework, is proposed to address the challenges of pre-training in robotic control tasks. By integrating a warm-up phase for the retrieval network and a shared training architecture, WINFlowNets enables co-training of both networks. Experiments in simulated robotic environments demonstrate improved average reward and training stability compared to existing CFlowNets and Reinforcement Learning algorithms. The framework also exhibits strong adaptive capability in fault environments, making it suitable for dynamic and malfunction-prone robotic systems. These findings highlight WINFlowNets' potential for deployment in real-world robotic applications.

Key Points

  • ▸ WINFlowNets integrates a warm-up phase for the retrieval network to bootstrap its policy.
  • ▸ The framework employs a shared training architecture and shared replay buffer for co-training both networks.
  • ▸ Experiments demonstrate improved performance and adaptive capability in fault environments.

Merits

Strength in Addressing Pre-training Challenges

WINFlowNets effectively addresses the challenges of pre-training in robotic control tasks by enabling co-training of both networks.

Improved Performance and Stability

Experiments demonstrate improved average reward and training stability compared to existing CFlowNets and Reinforcement Learning algorithms.

Demerits

Limited Exploratory Analysis

The article does not provide an in-depth exploratory analysis of the proposed framework's performance in various robotic control tasks.

Lack of Real-world Deployment

The framework's performance in real-world robotic applications is not evaluated in the article.

Expert Commentary

The proposed WINFlowNets framework demonstrates significant potential for addressing the challenges of pre-training in robotic control tasks. By integrating a warm-up phase for the retrieval network and a shared training architecture, WINFlowNets enables co-training of both networks, resulting in improved performance and stability. The framework's ability to adapt to fault environments is particularly noteworthy, highlighting the need for policy adjustments in robotic control tasks. While the article provides a comprehensive evaluation of WINFlowNets' performance in simulated robotic environments, further research is needed to evaluate its performance in real-world applications. Additionally, exploring the framework's potential in other domains, such as computer vision and natural language processing, could lead to exciting new applications.

Recommendations

  • ✓ Further research should be conducted to evaluate WINFlowNets' performance in real-world robotic applications.
  • ✓ The framework's potential in other domains, such as computer vision and natural language processing, should be explored.

Sources

Related Articles