465 citations · 879 across the 5 of their papers we have counts for
8 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…
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…
D2RL: Deep Dense Architectures in Reinforcement Learning
Samarth Sinha, Homanga Bharadhwaj, Aravind Srinivas +1
While improvements in deep learning architectures have played a crucial role in improving the state of supervised and unsupervised learning in computer vision and natural language…
Reinforcement Learning with Augmented Data
Michael Laskin, Kimin Lee, Adam Stooke +3
Learning from visual observations is a fundamental yet challenging problem in Reinforcement Learning (RL). Although algorithmic advances combined with convolutional neural networks…
CURL: Contrastive Unsupervised Representations for Reinforcement Learning
Aravind Srinivas, Michael Laskin, Pieter Abbeel
We present CURL: Contrastive Unsupervised Representations for Reinforcement Learning. CURL extracts high-level features from raw pixels using contrastive learning and performs off-…