Structure-Aware Transformer Policy for Inhomogeneous Multi-Task Reinforcement Learning

no code implementations • ICLR 2022 • Sunghoon Hong, Deunsol Yoon, Kee-Eung Kim

We empirically show that the morphological information is crucial for modular reinforcement learning, substantially outperforming prior state-of-the-art methods on multi-task learning as well as transfer learning settings with different state and action space dimensions.

Multi-Task Learning reinforcement-learning +1

Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic

no code implementations • ICLR 2021 • Deunsol Yoon, Sunghoon Hong, Byung-Jun Lee, Kee-Eung Kim

Safe and reliable electricity transmission in power grids is crucial for modern society.

Management Reinforcement Learning (RL)

Mature GAIL: Imitation Learning for Low-level and High-dimensional Input using Global Encoder and Cost Transformation

no code implementations • 7 Sep 2019 • Wonsup Shin, Hyolim Kang, Sunghoon Hong

In this paper, we propose a new algorithm based on the GAIL framework that includes a global encoder and the reward penalization mechanism.

Imitation Learning