Adnan KhanAdnanProduct & AI
San Francisco Bay Area
Get in touch

Source note2 min read

Expert Behavior Prior Reinforcement Learning

Source

arXiv

arxiv.org/abs/2607.21302v1

Read the source

Source

Plain-Language Brief

The paper proposes a new method called Expert Behavior Prior (EBP) for behavior prior reinforcement learning (BPRL). EBP aims to improve the effectiveness of policy priors by generating expert policy priors directly from the online replay buffer, rather than relying on pre-collected expert trajectories. This is done using a Q-guided conditional variational autoencoder (Q-CVAE) and an expert policy guidance (EPG) mechanism. The experiments show that EBP outperforms state-of-the-art online RL algorithms in terms of sample efficiency and stable convergence.

What the Document Says

  • The paper discusses a method called Expert Behavior Prior (EBP) for behavior prior reinforcement learning (BPRL).
  • EBP aims to address the limitations of existing BPRL methods by generating expert policy priors directly from the online replay buffer.
  • This is achieved using a Q-guided conditional variational autoencoder (Q-CVAE).
  • An expert policy guidance (EPG) mechanism is also proposed.
  • The paper integrates a policy gradient correction (PGC) module to harmonize Q-guidance with expert supervision.
  • Experiments show that EBP outperforms state-of-the-art online RL algorithms in terms of sample efficiency and stable convergence.

Key Concepts Mentioned

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

Claims Made by the Document

  • EBP addresses the limitations of existing BPRL methods.
  • EBP outperforms state-of-the-art online RL algorithms.

Evidence Used

  • The text discusses the proposed EBP method and its benefits.

Methods / Approach

  • Q-guided conditional variational autoencoder (Q-CVAE)
  • expert policy guidance (EPG)
  • policy gradient correction (PGC)

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not discuss potential limitations of the proposed method.

Open Questions

  • What are the specific details of the Q-guided conditional variational autoencoder (Q-CVAE) and expert policy guidance (EPG) mechanisms?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper focuses on explaining the benefits and principles of EBP, but it does not provide detailed explanations or equations for the underlying methods.

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.