用于预测、表征与控制的掩码轨迹模型（Masked Trajectory Models）
Masked Trajectory Models for Prediction, Representation, and Control
AI总结：
该研究提出通用序贯决策模型掩码轨迹模型（MTM），通过随机掩码训练实现多任务能力，在连续控制任务中性能匹配或优于专用模型，还能加速传统RL学习，离线RL基准表现媲美专用算法。
摘要：
我们提出掩码轨迹模型（Masked Trajectory Models，MTM），作为序贯决策的通用抽象模型。MTM接收一条轨迹（例如状态-动作序列），目标是基于同一条轨迹的随机子集来重建整条轨迹。通过采用高度随机化的掩码模式进行训练，MTM能够学习到具备多种功能的通用网络，仅需在推理阶段选择合适的掩码，就能实现不同的角色或能力。例如，同一个MTM网络既可以用作前向动力学模型、逆动力学模型，甚至还能作为离线强化学习（RL）智能体。我们在多项连续控制任务中开展了大量实验，结果表明，同一套权重的MTM网络，性能能够匹配甚至优于为上述各类功能专门训练的专用网络。此外，我们发现MTM学习到的状态表征能够显著提升传统强化学习算法的学习速度。最后，在离线RL基准测试中，尽管MTM是一种通用自监督学习方法，不包含任何显式的RL组件，但其性能仍可与专用离线RL算法相媲美。代码已开源至https://github.com/facebookresearch/mtm。
英文摘要：
We introduce Masked Trajectory Models (MTM) as a generic abstraction for sequential decision making. MTM takes a trajectory, such as a state-action sequence, and aims to reconstruct the trajectory conditioned on random subsets of the same trajectory. By training with a highly randomized masking pattern, MTM learns versatile networks that can take on different roles or capabilities, by simply choosing appropriate masks at inference time. For example, the same MTM network can be used as a forward dynamics model, inverse dynamics model, or even an offline RL agent. Through extensive experiments in several continuous control tasks, we show that the same MTM network -- i.e. same weights -- can match or outperform specialized networks trained for the aforementioned capabilities. Additionally, we find that state representations learned by MTM can significantly accelerate the learning speed of traditional RL algorithms. Finally, in offline RL benchmarks, we find that MTM is competitive with specialized offline RL algorithms, despite MTM being a generic self-supervised learning method without any explicit RL components. Code is available at https://github.com/facebookresearch/mtm
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
