465 citations · 627 across the 7 of their papers we have counts for
11 papers · 1 filter
Multi-Environment Pretraining Enables Transfer to Action Limited Datasets
David Venuto, Sherry Yang, Pieter Abbeel +3
Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications. In reinforcement learnin…
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…
Model-Based Reinforcement Learning via Latent-Space Collocation
Oleh Rybkin, Chuning Zhu, Anusha Nagabandi +3
The ability to plan into the future while utilizing only raw high-dimensional observations, such as images, can provide autonomous agents with broad capabilities. Visual model-base…
Pretrained Transformers as Universal Computation Engines
Kevin Lu, Aditya Grover, Pieter Abbeel +1
We investigate the capability of a transformer pretrained on natural language to generalize to other modalities with minimal finetuning -- in particular, without finetuning of the…
Reset-Free Lifelong Learning with Skill-Space Planning
Kevin Lu, Aditya Grover, Pieter Abbeel +1
The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail…
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic Control
Wenlong Huang, Igor Mordatch, Deepak Pathak
Reinforcement learning is typically concerned with learning control policies tailored to a particular agent. We investigate whether there exists a single global policy that can gen…