Scenic4RL: Programmatic Modeling and Generation of Reinforcement Learning Environments

no code implementations • 18 Jun 2021 • Abdus Salam Azad, Edward Kim, Qiancheng Wu, Kimin Lee, Ion Stoica, Pieter Abbeel, Sanjit A. Seshia

To showcase the benefits, we interfaced SCENIC to an existing RTS environment Google Research Football(GRF) simulator and introduced a benchmark consisting of 32 realistic scenarios, encoded in SCENIC, to train RL agents and testing their generalization capabilities.

reinforcement-learning Reinforcement Learning (RL)

SegAttnGAN: Text to Image Generation with Segmentation Attention

no code implementations • 25 May 2020 • Yuchuan Gou, Qiancheng Wu, Minghao Li, Bo Gong, Mei Han

In this paper, we propose a novel generative network (SegAttnGAN) that utilizes additional segmentation information for the text-to-image synthesis task.

Segmentation Text-to-Image Generation