Hybrid Pretraining Boosts Efficiency and Stability of Offline Reinforcement Learning

Global: Hybrid Pretraining Boosts Efficiency and Stability of Offline Reinforcement Learning

Overview

A study posted to arXiv on June 19, 2024, and revised on December 28, 2025, introduces a hybrid offline reinforcement‑learning framework that first imitates a behavior policy before applying off‑policy improvement. The work is authored by Adam Jelley, Trevor McInroe, Sam Devlin, and Amos Storkey, and it aims to combine the computational simplicity of imitation learning with the performance gains of off‑policy reinforcement learning.

Background

Offline reinforcement learning seeks to learn effective policies from fixed datasets without further environment interaction. While supervised imitation‑based methods are popular for their stable and efficient training, they are inherently limited by the quality of the behavior policy that generated the data. Conversely, off‑policy algorithms can surpass the behavior policy but often suffer from high computational cost and instability due to temporal‑difference bootstrapping.

Methodology

The authors propose a two‑stage approach: an actor network is first pre‑trained using behavior cloning, and a critic network is initialized with a supervised Monte‑Carlo value error. After this supervised phase, standard off‑policy reinforcement‑learning updates are applied to refine the policy. This design leverages the fast convergence of supervised learning while retaining the capacity for policy improvement.

Experimental Findings

Benchmarks on widely used offline RL suites demonstrate that the hybrid method reduces overall training time by a substantial margin compared with baseline off‑policy algorithms. The authors also report increased training stability, noting fewer divergent runs and smoother learning curves across tasks.

Comparison to Existing Approaches

Relative to pure imitation learning, the proposed technique achieves higher final performance by overcoming the ceiling imposed by the behavior policy. Compared with purely off‑policy methods, it delivers comparable or better returns while requiring fewer compute resources, suggesting a practical advantage for resource‑constrained settings.

Code Availability

The implementation code is publicly released via a repository linked in the paper, enabling replication and further experimentation by the research community.

Implications and Future Work

By demonstrating that a modest amount of supervised pre‑training can accelerate and stabilize offline reinforcement learning, the study points to a promising direction for deploying RL solutions in safety‑critical domains where data collection is expensive. The authors suggest extending the approach to larger-scale datasets and exploring alternative value‑estimation strategies as next steps.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.