标题： TD-MPC-Opt：基于模型的多任务强化学习智能体的蒸馏 显示英文标题

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

摘要： 我们提出了一种基于模型的强化学习中的知识迁移新方法，解决了在资源受限环境中部署大型世界模型的关键挑战。 我们的方法在MT30基准上高效地将高容量多任务智能体（317M参数）蒸馏为一个紧凑模型（1M参数），显著提高了在各种任务上的性能。 我们蒸馏后的模型实现了最先进的归一化得分28.45，超过了原始1M参数模型的得分18.93。 这一改进证明了我们的蒸馏技术能够捕捉和整合复杂的多任务知识。 我们进一步通过FP16后训练量化优化了蒸馏模型，将其大小减少了$\sim$50%。 我们的方法解决了实际部署限制，并为大型世界模型中的知识表示提供了见解，为机器人和其他资源受限应用中的更高效、更易访问的多任务强化学习系统铺平了道路。 代码可在https://github.com/dmytro-kuzmenko/td-mpc-opt获取。 显示英文摘要

摘要： 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.