Combining Trained Models in Reinforcement Learning

2026-05-05 · Source: cs.NE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, long

Summary

A PRISMA-guided systematic review analyzed 15 empirical studies on pretrained knowledge reuse in deep reinforcement learning (DRL), focusing on transfer, distillation, ensemble, and federated training methods. The review, which screened 570 unique records from IEEE Xplore, ACM Digital Library, and citation tracing, found that positive results are concentrated where source and target tasks share substantial structure or include explicit gating mechanisms. Evidence for ensembles and federated aggregation is sparse, primarily limited to narrow settings. The analysis also revealed that compute-matched comparisons are rare, weakening claims about efficiency gains. The study contributes a focused review scope, a synthesis of empirical evidence, and a provisional "independence spectrum" for describing diversity among reused models.

Key takeaway

Research scientists developing DRL systems should prioritize knowledge reuse strategies that explicitly account for source-target task similarity or incorporate gating/alignment mechanisms. Be cautious with broad claims about ensemble or federated DRL benefits, as empirical evidence is currently limited. When evaluating efficiency, ensure your benchmarks include compute-matched comparisons against strong from-scratch baselines to validate performance gains accurately.

Key insights

Pretrained knowledge reuse in DRL succeeds when tasks align or explicit alignment mechanisms are used.

Principles

• Reuse benefits from structural overlap between tasks.
• Explicit alignment manages task mismatch.
• Compute reporting is often insufficient for efficiency claims.

Method

A PRISMA-guided systematic review synthesized 15 empirical DRL studies, analyzing source-target similarity, model diversity, and comparison fairness against from-scratch baselines.

In practice

• Prioritize methods with explicit gating for transfer.
• Consider task similarity for effective knowledge reuse.
• Report compute budgets for credible efficiency claims.

Topics

• Pretrained Knowledge Reuse
• Deep Reinforcement Learning
• Transfer Learning
• Policy Distillation
• Ensemble Reinforcement Learning

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.NE updates on arXiv.org.