Agency-Transferring Model-Free Policy Enhancement: Embedding Baseline Policies into RL Training

Forum topic · 小凯 · 2026-06-10

Summary

Researchers Anton Bolychev, Georgiy Malaniya, and Sinan Ibrahim propose an agency-transferring, model-free policy enhancement technique for reinforcement learning (arXiv:2506.04858, June 2025). Instead of training RL policies from scratch—which demands careful reward design, extensive tuning, and heavy computation—the method embeds an existing functional but suboptimal baseline policy into the training process. At each step, an arbitration mechanism blends the baseline and a trainable learning policy, initially relying heavily on the baseline and progressively transferring decision-making agency to the learning policy. By the end of training, the learning policy becomes a standalone neural network requiring no baseline support. The authors formalize baseline functionality as reaching a goal set with high probability and maintaining it, and they prove theoretical guarantees, including an explicit lower bound on the standalone policy's goal-reaching probability. Experiments on continuous control benchmarks show returns comparable to or exceeding competing methods while maintaining the highest goal-reaching rate throughout training.

Overview

Field: Machine Learning (Reinforcement Learning)
Authors: Anton Bolychev, Georgiy Malaniya, Sinan Ibrahim
Published: 2025-06-06
arXiv: 2506.04858

Abstract (translated summary)

Training reinforcement learning (RL) policies from scratch is costly: it requires careful reward and environment design, extensive tuning, and substantial computation. Yet many control problems already have a functional but suboptimal policy available as a baseline. This paper proposes a method for embedding such a baseline into the RL training process, simultaneously improving training efficiency relative to from-scratch methods and producing a learning policy that outperforms the baseline.

At each step, the method arbitrates between the baseline policy and a trainable learning policy, initially relying strongly on the baseline policy and then progressively transferring agency to the learning policy. By the end of training, the learning policy is a standalone neural network that operates without the baseline policy.

Theoretical Contributions

The authors formalize what it means for a baseline policy to be functional: under this policy, the agent reaches a goal set with high probability and stays within it. The proposed arbitration mechanism is designed to exploit this property, achieving high goal-reaching rates early in training.

The theoretical analysis provides a formal explanation of this behavior under stated assumptions and extends it to the final baseline-free phase, deriving an explicit lower bound on the goal-reaching probability of the independent learning policy.

Experimental Results

Experiments on continuous control benchmarks show that the method achieves returns comparable to or higher than competing approaches, while maintaining the highest goal-reaching rate throughout the entire training process.

Key Takeaways

Embeds an existing suboptimal baseline policy into RL training rather than starting from scratch.
Agency transfers gradually from baseline to a trainable learning policy during training.
Final policy is a standalone neural network with no runtime dependence on the baseline.
Formal goal-reaching guarantees, including an explicit lower bound for the final policy.
State-of-the-art goal-reaching rates maintained across training on continuous control benchmarks.