Reinforcement Learning
SimBaV2: Hyperspherical Normalization for Scalable Deep Reinforcement Learning
Hojoon Lee, Youngdo Lee, Takuma Seno, Donghu Kim, Peter Stone, Jaegul Choo.
Under review.
arXiv / project page / github

We further regularize SimBa architecture by projecting both parameters and features onto a hypersphere, leading to better scaling properties in model size and compute.

Reinforcement Learning
SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learning
Hojoon Lee, Dongyoon Hwang, Donghu Kim, Hyunseung Kim, Jun Jet Tai, Kaushik Subramanian, Peter R. Wurman, Jaegul Choo, Peter Stone, Takuma Seno.
ICLR'25, Spotlight.
arXiv / project page / github

We propose a well-regularized architecture that avoids overfitting, allowing parameter and compute scale up in RL.

Reinforcement Learning Skill Discovery
Do’s and Don’ts: Learning Desirable Skills with Instruction Videos
Hyunseung Kim, Byungkun Lee, Hojoon Lee, Dongyoon Hwang, Donghu Kim, Jaegul Choo
NeurIPS'24.
arXiv / project page

We present DoDont, a skill discovery algorithm that learns diverse behaviors while following the behaviors in "do" videos while avoiding the behaviors in "don't" videos.

Reinforcement Learning Pre-training
ATARI-PB: Investigating Pre-Training Objectives for Generalization in Pixel-Based RL
Donghu Kim*, Hojoon Lee*, Kyungmin Lee*, Dongyoon Hwang, Jaegul Choo.
ICML'24.
arXiv / project page / github / poster

We investigate which pre-training objectives are beneficial for in-distribution, near-out-of-distribution, and far-out-of-distribution generalization in visual reinforcement learning.

Reinforcement Learning Plasticity
Slow and Steady Wins the Race: Maintaining Plasticity with Hare and Tortoise Networks
Hojoon Lee, Hyeonseo Cho, Hyunseung Kim, Donghu Kim, Dugki Min, Jaegul Choo, Clare Lyle.
ICML'24.
arXiv / github

To allow the network to continually adapt and generalize, we introduce Hare and Tortoise architecture, inspired by the complementary learning system of the human brain.