1 citations · 1 across the 4 of their papers we have counts for
5 papers
Chunk-Guided Q-Learning
Gwanwoo Song, Kwanyoung Park, Youngwoon Lee
In offline reinforcement learning (RL), single-step temporal-difference (TD) learning can suffer from bootstrapping error accumulation over long horizons. Action-chunked TD methods…
Latent Policy Steering through One-Step Flow Policies
Hokyun Im, Andrey Kolobov, Jianlong Fu +1
Offline reinforcement learning (RL) allows robots to learn from offline datasets without risky exploration. Yet, offline RL's performance often hinges on a brittle trade-off betwee…
Scalable Offline Model-Based RL with Action Chunks
Kwanyoung Park, Seohong Park, Youngwoon Lee +1
In this paper, we study whether model-based reinforcement learning (RL), in particular model-based value expansion, can provide a scalable recipe for tackling complex, long-horizon…
TLDR: Unsupervised Goal-Conditioned RL via Temporal Distance-Aware Representations
Junik Bae, Kwanyoung Park, Youngwoon Lee
Unsupervised goal-conditioned reinforcement learning (GCRL) is a promising paradigm for developing diverse robotic skills without external supervision. However, existing unsupervis…
Model-based Offline Reinforcement Learning with Lower Expectile Q-Learning
Kwanyoung Park, Youngwoon Lee
Model-based offline reinforcement learning (RL) is a compelling approach that addresses the challenge of learning from limited, static data by generating imaginary trajectories usi…