Learning Dynamics Model in Reinforcement Learning by Incorporating the Long Term Future

International Conference on Learning Representations (ICLR)


In model-based reinforcement learning, the agent interleaves between model learning and planning. These two components are inextricably intertwined. If the model is not able to provide sensible long-term prediction, the executed planner would exploit model flaws, which can yield catastrophic failures. This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration. To this end, we build a latent-variable autoregressive model by leveraging recent ideas in variational inference. We argue that forcing latent variables to carry future information through an auxiliary task substantially improves long-term predictions. Moreover, by planning in the latent space, the planner’s solution is ensured to be within regions where the model is valid. An exploration strategy can be devised by searching for unlikely trajectories under the model. Our method achieves higher reward faster compared to baselines on a variety of tasks and environments in both the imitation learning and model-based reinforcement learning settings.

See our code on GitHub

Related Publications

All Publications

SIGDIAL - August 1, 2021

Annotation Inconsistency and Entity Bias in MultiWOZ

Kun Qian, Ahmad Berrami, Zhouhan Lin, Ankita De, Alborz Geramifard, Zhou Yu, Chinnadhurai Sankar

Uncertainty and Robustness in Deep Learning Workshop at ICML - August 1, 2020

Tilted Empirical Risk Minimization

Tian Li, Ahmad Beirami, Maziar Sanjabi, Virginia Smith

arxiv - November 1, 2020

The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes

Douwe Kiela, Hamed Firooz, Aravind Mohan, Vedanuj Goswami, Amanpreet Singh, Pratik Ringshia, Davide Testuggine

ICML - July 24, 2021

Using Bifurcations for Diversity in Differentiable Games

Jonathan Lorraine, Jack Parker-Holder, Paul Vicol, Aldo Pacchiano, Luke Metz, Tal Kachman, Jakob Foerster

To help personalize content, tailor and measure ads, and provide a safer experience, we use cookies. By clicking or navigating the site, you agree to allow our collection of information on and off Facebook through cookies. Learn more, including about available controls: Cookies Policy