An Agency-Transferring Model-Free Policy Enhancement Technique

Problem
The paper addresses the inefficiencies in training reinforcement learning (RL) policies from scratch, which often require extensive computational resources, careful reward design, and fine-tuning. Many control problems already have suboptimal but functional baseline policies available. This work proposes a method to leverage these existing policies to enhance training efficiency and policy performance. The authors note that this is a preprint and has not undergone peer review.

Method
The proposed method introduces an agency-transferring mechanism that integrates a baseline policy into the RL training process. The approach begins with a strong reliance on the baseline policy, gradually transferring agency to a trainable learning policy. The arbitration mechanism is designed to exploit the baseline’s functional properties, ensuring that the agent can reach a specified goal with high probability. The learning policy is represented as a standalone neural network by the end of training, operating independently of the baseline. Theoretical analysis is provided to formalize the behavior of the learning policy under certain assumptions, including deriving explicit lower bounds for the goal-reaching probability in the baseline-free regime. The authors do not disclose specific architectural details, loss functions, or training compute requirements.

Results
Empirical evaluations on continuous-control benchmarks demonstrate that the proposed method achieves competitive returns, matching or exceeding those of existing state-of-the-art approaches. Notably, the method maintains the highest goal-reaching rates throughout the training process, including the final phase where the learning policy operates without baseline support. Specific performance metrics are not detailed in the summary, but the results indicate a significant improvement in training efficiency and effectiveness compared to traditional methods.

Limitations
The authors acknowledge that their method relies on the availability of a functional baseline policy, which may not be present in all scenarios. Additionally, the theoretical guarantees provided are contingent on the stated assumptions, which may limit generalizability. The paper does not address potential issues related to the scalability of the method or its performance in highly dynamic environments where baseline policies may not be as effective.

Why it matters
This work has significant implications for the field of reinforcement learning, particularly in scenarios where baseline policies are available. By improving training efficiency and policy performance, this method can facilitate the deployment of RL in real-world applications where computational resources are limited. The findings contribute to the ongoing discourse on policy enhancement techniques in RL, as discussed in related literature. For further details, see the full paper available on arXiv.