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Source note3 min read

Expert Behavior Prior Reinforcement Learning

Source

arXiv

arxiv.org/abs/2607.21302v1

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Plain-Language Brief

A new method called Expert Behavior Prior Reinforcement Learning (EBP) is proposed to improve the sample efficiency in online reinforcement learning. This method generates expert policy priors directly from the online replay buffer using a Q-guided conditional variational autoencoder (Q-CVAE). The EBP algorithm also includes an expert policy guidance (EPG) mechanism and a policy gradient correction (PGC) module to enhance policy exploitation, promote stable and consistent policy improvement, and outperform state-of-the-art online RL algorithms.

What the Document Says

  • The paper discusses Expert Behavior Prior Reinforcement Learning (BPRL), a method for improving sample efficiency in online reinforcement learning.
  • Most existing BPRL methods rely on static offline datasets, which can suffer from low data diversity and suboptimal trajectory quality.
  • The proposed EBP algorithm uses a Q-guided conditional variational autoencoder (Q-CVAE) to generate expert policy priors directly from the online replay buffer.
  • EPG mechanism is proposed to select expert actions from a generative support set in EBP.
  • PGC module is integrated to harmonize Q-guidance with expert supervision in EBP.
  • Experiments on robotic control and industrial control benchmarks show that EBP outperforms state-of-the-art online RL algorithms.

Key Concepts Mentioned

  • Expert Behavior Prior Reinforcement Learning (EBP)
  • Q-guided conditional variational autoencoder (Q-CVAE)
  • Online reinforcement learning (RL)
  • Policy priors
  • Offline demonstrations
  • Sample efficiency
  • Robotic control
  • Industrial control
  • Expert policy guidance (EPG)
  • Policy gradient correction (PGC)

Claims Made by the Document

  • EBP addresses limitations of existing offline pre-training methods.
  • EBP generates high-value actions for guiding policy updates without relying on pre-collected expert trajectories.
  • EBP outperforms state-of-the-art online RL algorithms.

Evidence Used

  • The text describes the EBP algorithm and its components, as well as the experimental results showing its performance.

Methods / Approach

  • Expert Behavior Prior Reinforcement Learning
  • Q-guided conditional variational autoencoder
  • Expert policy guidance (EPG)
  • Policy gradient correction (PGC)

Limitations Stated by the Authors

The limitations of the proposed method are not discussed in the paper.

Limitations Noted by the Agent

Agent interpretation — not from the source.

It is unclear if the proposed EBP algorithm can be applied to other domains or if it has any practical limitations.

Open Questions

  • Can the EBP algorithm be adapted to other reinforcement learning problems?
  • What are the potential limitations of the EBP algorithm in practice?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper presents a new method for improving sample efficiency in online reinforcement learning called Expert Behavior Prior Reinforcement Learning (EBP). The method generates expert policy priors directly from the online replay buffer using a Q-guided conditional variational autoencoder (Q-CVAE). To further enhance policy exploitation, an expert policy guidance (EPG) mechanism and a policy gradient correction (PGC) module are proposed. Experiments demonstrate that EBP outperforms state-of-the-art online RL algorithms.

Raw Abstract / Excerpt

Behavior prior reinforcement learning (BPRL) has emerged as a promising paradigm to improve sample efficiency in online reinforcement learning (RL) by leveraging policy priors derived from offline demonstrations. However, most existing BPRL methods rely on static offline datasets, which often suffer from low data diversity and suboptimal trajectory quality. This reliance restricts the effectiveness of policy priors, hindering both policy exploitation and stability during online training. Consequently, agents are prone to inefficient exploration and unstable learning dynamics. To address these limitations, we deviate from existing offline pre-training methods and propose an Expert Behavior Prior (EBP) algorithm. Specifically, we introduce a Q-guided conditional variational autoencoder (Q-CVAE) that learns to generate expert policy priors directly from the online replay buffer. This enables the generation of high-value actions for guiding policy updates without relying on pre-collected expert trajectories. To further enhance policy exploitation, we propose an expert policy guidance (EPG) mechanism that selects expert actions from a generative support set, and we integrate a policy gradient correction (PGC) module to harmonize Q-guidance with expert supervision, promoting stable and consistent policy improvement. Extensive experiments conducted on robotic control (Gym, PyBullet) and industrial control (DMControl) benchmarks demonstrate that EBP significantly outperforms state-of-the-art online RL algorithms, achieving higher sample efficiency and more stable convergence.