Combining Trained Models in Reinforcement Learning

📰 ArXiv cs.AI

Learn to combine trained models in reinforcement learning to improve transfer and reduce sample cost

advanced Published 5 May 2026

Action Steps

Implement ensemble methods to combine the predictions of multiple trained models
Use transfer learning to adapt a pre-trained model to a new task
Apply distillation methods to transfer knowledge from a complex model to a simpler one
Configure federated training to combine models trained on different datasets or environments
Test the combined model on a target task to evaluate its performance

Who Needs to Know This

Researchers and engineers working on reinforcement learning projects can benefit from this technique to improve the efficiency and effectiveness of their models

Key Insight

💡 Combining trained models can improve the efficiency and effectiveness of reinforcement learning

Key Takeaways

Learn to combine trained models in reinforcement learning to improve transfer and reduce sample cost

Full Article

Title: Combining Trained Models in Reinforcement Learning

Abstract:
arXiv:2605.02159v1 Announce Type: cross Abstract: Deep reinforcement learning (DRL) has delivered strong results in domains such as Atari and Go, but it still suffers from high sample cost and weak transfer beyond the training setting. A common response is to reuse information from previously trained models through transfer, distillation, ensemble methods, or federated training instead of learning each target task from random initialization. The literature on these mechanisms is fragmented, and

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