Video Prediction Models as Rewards for Reinforcement Learning

Neural Information Processing Systems (NeurIPS 2023)

Alejandro Escontrela    Ademi Adeniji    Wilson Yan    Ajay Jain    Xue Bin Peng    Ken Goldberg    Youngwoon Lee    Danijar Hafner    Pieter Abbeel

University of California, Berkeley


Specifying reward signals that allow agents to learn complex behaviors is a longstanding challenge in reinforcement learning. A promising approach is to extract preferences for behaviors from unlabeled videos, which are widely available on the internet. We present Video Prediction Rewards (VIPER), an algorithm that leverages pretrained video prediction models as action-free reward signals for reinforcement learning. Specifically, we first train an autoregressive transformer on expert videos and then use the video prediction likelihoods as reward signals for a reinforcement learning agent. VIPER enables expert-level control without programmatic task rewards across a wide range of DMC, Atari, and RLBench tasks. Moreover, generalization of the video prediction model allows us to derive rewards for an out-of-distribution environment where no expert data is available, enabling cross-embodiment generalization for tabletop manipulation. We see our work as starting point for scalable reward specification from unlabeled videos that will benefit from the rapid advances in generative modeling.

Paper: [PDF]       Code: [GitHub]       Webpage: [Link]       Preprint: [arXiv]


  journal = {Neural Information Processing Systems},
  author = {Escontrela, Alejandro and Adeniji, Ademi and Yan, Wilson and Jain, Ajay and Peng, Xue Bin and Goldberg, Ken and Lee, Youngwoon and Hafner, Danijar and Abbeel, Pieter},
  keywords = {Artificial Intelligence (cs.AI)},
  title = {Video Prediction Models as Rewards for Reinforcement Learning},
  publisher = {arXiv},
  copyright = {Creative Commons Attribution 4.0 International},
  year = {2023},
  eprint = {2305.14343},
  archiveprefix = {arXiv},
  primaryclass = {cs.LG},