7 citations · 11 across the 13 of their papers we have counts for
5 papers · 1 filter
Posterior Behavioral Cloning: Pretraining BC Policies for Efficient RL Finetuning
Andrew Wagenmaker, Perry Dong, Raymond Tsao +2
Standard practice across domains from robotics to language is to first pretrain a policy on a large-scale demonstration dataset, and then finetune this policy, typically with reinf…
Value Flows
Perry Dong, Chongyi Zheng, Chelsea Finn +2
While most reinforcement learning methods today flatten the distribution of future returns to a single scalar value, distributional RL methods exploit the return distribution to pr…
EXPO: Stable Reinforcement Learning with Expressive Policies
Perry Dong, Qiyang Li, Dorsa Sadigh +1
We study the problem of training and fine-tuning expressive policies with online reinforcement learning (RL) given an offline dataset. Training expressive policy classes with onlin…
Reinforcement Learning via Implicit Imitation Guidance
Perry Dong, Alec M. Lessing, Annie S. Chen +1
We study the problem of sample efficient reinforcement learning, where prior data such as demonstrations are provided for initialization in lieu of a dense reward signal. A natural…
What Matters for Batch Online Reinforcement Learning in Robotics?
Perry Dong, Suvir Mirchandani, Dorsa Sadigh +1
The ability to learn from large batches of autonomously collected data for policy improvement -- a paradigm we refer to as batch online reinforcement learning -- holds the promise…