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Source note3 min read

Active Offline-to-Online Reinforcement Learning

Source

arXiv

arxiv.org/abs/2607.11720v1

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Source

Plain-Language Brief

Researchers propose an active policy selection method for fine-tuning in offline-to-online reinforcement learning (O2O-RL) settings, where the goal is to allocate a limited online interaction budget effectively among multiple policies. The proposed approach balances between policy evaluation and fine-tuning by using upper-confidence bounds derived from locally linear performance forecasts. The results show that the new method outperforms existing O2O-RL baselines.

What the Document Says

  • Offline-to-Online Reinforcement Learning (O2O-RL) is a paradigm for training policies from large datasets and fine-tuning them through limited online interaction. The standard pipeline involves training multiple candidate policies offline, evaluating them, and deploying the best one.
  • The paper addresses the problem of policy selection for fine-tuning under a limited interaction budget in O2O-RL settings. To the authors' knowledge, this is the first work to tackle this problem.
  • The method proposed in the paper balances the trade-off between allocating online interactions to policy evaluation and fine-tuning by actively selecting policies for fine-tuning based on upper-confidence bounds derived from locally linear performance forecasts.
  • Across a diverse range of experiments, the new approach outperforms existing O2O-RL baselines.
  • The results indicate that actively selecting and fine-tuning policies with a limited online interaction budget is more effective than committing to a single policy or dividing the budget equally among all policies.
  • The paper suggests that the proposed framework advances offline RL toward practical deployment in real-world systems where online interaction is costly or risky.

Key Concepts Mentioned

  • Offline-to-Online Reinforcement Learning (O2O-RL)
  • Policy selection
  • Fine-tuning
  • Upper-confidence bounds
  • Locally linear performance forecasts

Claims Made by the Document

  • The proposed approach outperforms existing O2O-RL baselines.
  • Actively selecting and fine-tuning policies with a limited online interaction budget is more effective than committing to a single policy or dividing the budget equally among all policies.

Evidence Used

  • Results across a diverse range of experiments show that the proposed approach outperforms existing O2O-RL baselines.
  • The results indicate that actively selecting and fine-tuning policies with a limited online interaction budget is more effective than committing to a single policy or dividing the budget equally among all policies.

Methods / Approach

  • Upper-confidence bounds derived from locally linear performance forecasts
  • Policy selection for fine-tuning under a limited interaction budget

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Background: Offline reinforcement learning (RL) enables effective policies to be trained from large, previously collected datasets and subsequently improved through limited online interaction. This offline-to-online RL (O2O-RL) paradigm is particularly promising in nonstationary domains where interaction is costly or potentially hazardous. Standard O2O-RL pipelines train multiple candidate policies offline, evaluate them using off-policy or online evaluation, and then deploy and fine-tune the policy with the highest estimated value. However, as in offline pretraining, fine-tuning performance is highly sensitive to the choice of algorithm and hyperparameters, making it risky to commit to a single policy. Objectives: We study active policy selection for fine-tuning under a limited interaction budget in O2O-RL settings. To our knowledge, this is the first work to address this problem. Methods: We formulate the problem by identifying a fundamental trade-off between allocating online interactions to policy evaluation, which helps identify high-performing policies, and allocating them to fine-tuning, which improves policy performance. We then propose an approach that balances this trade-off by actively selecting policies for fine-tuning based on upper-confidence bounds on their future performance. These bounds are derived from locally linear performance forecasts fitted to observations obtained through online evaluation. Results: Across a diverse range of experiments, the proposed approach consistently outperforms existing O2O-RL baselines. Conclusions: Actively selecting and fine-tuning policies uses limited online interaction budgets more effectively than either committing to a single policy or dividing the budget equally among all policies. Our framework also advances offline RL toward practical deployment in real-world systems where online interaction is costly or risky.