text: 25,71,1261100300678492160,1074633382452051969,Kimin,"New work with @young93k, Seunghyun Lee, @honglaklee, and Jinwoo Shin - Context-aware Dynamics Model for Generalization in Model-Based Reinforcement Learning!  Paper: <LINK> Project Page: <LINK> Code: <LINK> Main problem: We study how to learn a global dynamics model that can generalize across different dynamics <LINK> Method: We separate context encoding and transition inference, and propose various auxiliary tasks to extract contextual information effectively <LINK> Results 1: The proposed context-aware dynamics model significantly improves the generalization performances of baseline model-based methods. <LINK> Result 2: You can also utilize the learned context latent vector to improve the generalization abilities of model-free RL methods. <LINK>",https://arxiv.org/abs/2005.06800,"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. ","Context-aware Dynamics Model for Generalization in Model-Based