75 citations · 163 across the 19 of their papers we have counts for
20 papers
EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought
Yao Mu, Qinglong Zhang, Mengkang Hu +7
Embodied AI is a crucial frontier in robotics, capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work…
A Simple Decentralized Cross-Entropy Method
Zichen Zhang, Jun Jin, Martin Jagersand +2
Cross-Entropy Method (CEM) is commonly used for planning in model-based reinforcement learning (MBRL) where a centralized approach is typically utilized to update the sampling dist…
Dynamic Decision Frequency with Continuous Options
Amirmohammad Karimi, Jun Jin, Jun Luo +3
In classic reinforcement learning algorithms, agents make decisions at discrete and fixed time intervals. The duration between decisions becomes a crucial hyperparameter, as settin…
Build generally reusable agent-environment interaction models
Jun Jin, Hongming Zhang, Jun Luo
This paper tackles the problem of how to pre-train a model and make it generally reusable backbones for downstream task learning. In pre-training, we propose a method that builds a…
Auxiliary task discovery through generate-and-test
Banafsheh Rafiee, Sina Ghiassian, Jun Jin +3
In this paper, we explore an approach to auxiliary task discovery in reinforcement learning based on ideas from representation learning. Auxiliary tasks tend to improve data effici…
What makes useful auxiliary tasks in reinforcement learning: investigating the effect of the target policy
Banafsheh Rafiee, Jun Jin, Jun Luo +1
Auxiliary tasks have been argued to be useful for representation learning in reinforcement learning. Although many auxiliary tasks have been empirically shown to be effective for a…