TD-MPC-Opt: Distilling Model-Based Multi-Task Reinforcement Learning Agents

• URL: http://arxiv.org/abs/2507.01823v1
• Date: Wed, 02 Jul 2025 15:38:49 GMT
• Title: TD-MPC-Opt: Distilling Model-Based Multi-Task Reinforcement Learning Agents
• Authors: Dmytro Kuzmenko, Nadiya Shvai,
• Abstract summary: We present a novel approach to knowledge transfer in model-based reinforcement learning.<n>Our method efficiently distills a high-capacity multi-task agent into a compact model.<n>Our approach addresses practical deployment limitations and offers insights into knowledge representation in large world models.
• Score: 1.6574413179773757
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a novel approach to knowledge transfer in model-based reinforcement learning, addressing the critical challenge of deploying large world models in resource-constrained environments. Our method efficiently distills a high-capacity multi-task agent (317M parameters) into a compact model (1M parameters) on the MT30 benchmark, significantly improving performance across diverse tasks. Our distilled model achieves a state-of-the-art normalized score of 28.45, surpassing the original 1M parameter model score of 18.93. This improvement demonstrates the ability of our distillation technique to capture and consolidate complex multi-task knowledge. We further optimize the distilled model through FP16 post-training quantization, reducing its size by $\sim$50\%. Our approach addresses practical deployment limitations and offers insights into knowledge representation in large world models, paving the way for more efficient and accessible multi-task reinforcement learning systems in robotics and other resource-constrained applications. Code available at https://github.com/dmytro-kuzmenko/td-mpc-opt.

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