Model-based reinforcement learning (RL) enjoys several benefits, such as data-efficiency and planning, by learning a model of the environment's dynamics. However, learning a global model that can generalize across different dynamics is a challenging task. To tackle this problem, we decompose the task of learning a global dynamics model into two stages: (a) learning a context latent vector that captures the local dynamics, then (b) predicting the next state conditioned on it. In order to encode dynamics-specific information into the context latent vector, we introduce a novel loss function that encourages the context latent vector to be useful for predicting both forward and backward dynamics. The proposed method achieves superior generalization ability across various simulated robotics and control tasks, compared to existing RL schemes.

该论文提出了一种用于强化学习中学习动态全局模型的方法，通过将学习全局动态模型的任务分解为学习本地动态的上下文潜向量和条件预测下一个状态来实现此目的，并通过鼓励上下文潜向量在预测正向和反向动态方面有用来编码动态特定信息。该方法在各种模拟机器人控制任务中实现了优异的泛化能力，超过了现有的强化学习方案。

基于上下文感知的模型动态学习在强化学习中的泛化应用