Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning

Dmytro Kuzmenko; Nadiya Shvai

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arXiv · 2501.05329

Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning

Dmytro Kuzmenko·Nadiya Shvai

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Abstract

We propose an efficient knowledge transfer approach for model-based reinforcement learning, addressing the challenge of deploying large world models in resource-constrained environments. Our method distills a high-capacity multi-task agent (317M parameters) into a compact 1M parameter model, achieving state-of-the-art performance on the MT30 benchmark with a normalized score of 28.45, a substantial improvement over the original 1M parameter model's score of 18.93. This demonstrates the ability of our distillation technique to consolidate complex multi-task knowledge effectively. Additionally, we apply FP16 post-training quantization, reducing the model size by 50% while maintaining performance. Our work bridges the gap between the power of large models and practical deployment constraints, offering a scalable solution for efficient and accessible multi-task reinforcement learning in robotics and other resource-limited domains.

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BibTeX RIS

Dmytro Kuzmenko, Nadiya Shvai. 2025-01-09. Knowledge Transfer in Model-Based Reinforcement Learning Agents for Efficient Multi-Task Learning. https://arxiv.org/abs/2501.05329

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