1 code implementation • 27 May 2024 • Jaewoo Lee, Sujin Yun, Taeyoung Yun, Jinkyoo Park
Our results show that GTA, as a general data augmentation strategy, enhances the performance of widely used offline RL algorithms in both dense and sparse reward settings.
2 code implementations • 4 Oct 2023 • Minsu Kim, Taeyoung Yun, Emmanuel Bengio, Dinghuai Zhang, Yoshua Bengio, Sungsoo Ahn, Jinkyoo Park
Generative Flow Networks (GFlowNets) are amortized sampling methods that learn a distribution over discrete objects proportional to their rewards.
1 code implementation • 4 Oct 2023 • Minsu Kim, Joohwan Ko, Taeyoung Yun, Dinghuai Zhang, Ling Pan, Woochang Kim, Jinkyoo Park, Emmanuel Bengio, Yoshua Bengio
We find that the challenge is greatly reduced if a learned function of the temperature is used to scale the policy's logits directly.