906 citations · 2.6k across the 17 of their papers we have counts for
7 papers · 1 filter
Decision Transformer: Reinforcement Learning via Sequence Modeling
Lili Chen, Kevin Lu, Aravind Rajeswaran +6
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer ar…
VideoGPT: Video Generation using VQ-VAE and Transformers
Wilson Yan, Yunzhi Zhang, Pieter Abbeel +1
We present VideoGPT: a conceptually simple architecture for scaling likelihood based generative modeling to natural videos. VideoGPT uses VQ-VAE that learns downsampled discrete la…
Revisiting ResNets: Improved Training and Scaling Strategies
Irwan Bello, William Fedus, Xianzhi Du +5
Novel computer vision architectures monopolize the spotlight, but the impact of the model architecture is often conflated with simultaneous changes to training methodology and scal…
Scaling Local Self-Attention for Parameter Efficient Visual Backbones
Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas +3
Self-attention has the promise of improving computer vision systems due to parameter-independent scaling of receptive fields and content-dependent interactions, in contrast to para…
Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
Lili Chen, Kimin Lee, Aravind Srinivas +1
Recent advances in off-policy deep reinforcement learning (RL) have led to impressive success in complex tasks from visual observations. Experience replay improves sample-efficienc…
Reinforcement Learning with Latent Flow
Wenling Shang, Xiaofei Wang, Aravind Srinivas +4
Temporal information is essential to learning effective policies with Reinforcement Learning (RL). However, current state-of-the-art RL algorithms either assume that such informati…