19 citations · 54 across the 13 of their papers we have counts for
6 papers · 1 filter
Demonstration-free Autonomous Reinforcement Learning via Implicit and Bidirectional Curriculum
Jigang Kim, Daesol Cho, H. Jin Kim
While reinforcement learning (RL) has achieved great success in acquiring complex skills solely from environmental interactions, it assumes that resets to the initial state are rea…
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation
Daesol Cho, Seungjae Lee, H. Jin Kim
Current reinforcement learning (RL) often suffers when solving a challenging exploration problem where the desired outcomes or high rewards are rarely observed. Even though curricu…
SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning
Dongseok Shim, Seungjae Lee, H. Jin Kim
As previous representations for reinforcement learning cannot effectively incorporate a human-intuitive understanding of the 3D environment, they usually suffer from sub-optimal pe…
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
Seungjae Lee, Jigang Kim, Inkyu Jang +1
Hierarchical Reinforcement Learning (HRL) has made notable progress in complex control tasks by leveraging temporal abstraction. However, previous HRL algorithms often suffer from…
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning
Daesol Cho, Dongseok Shim, H. Jin Kim
Offline reinforcement learning (Offline RL) suffers from the innate distributional shift as it cannot interact with the physical environment during training. To alleviate such limi…
Automating Reinforcement Learning with Example-based Resets
Jigang Kim, J. hyeon Park, Daesol Cho +1
Deep reinforcement learning has enabled robots to learn motor skills from environmental interactions with minimal to no prior knowledge. However, existing reinforcement learning al…