69 citations · 119 across the 4 of their papers we have counts for
4 papers
Reusability and Transferability of Macro Actions for Reinforcement Learning
Yi-Hsiang Chang, Kuan-Yu Chang, Henry Kuo +1
Conventional reinforcement learning (RL) typically determines an appropriate primitive action at each timestep. However, by using a proper macro action, defined as a sequence of pr…
Diversity-Driven Exploration Strategy for Deep Reinforcement Learning
Zhang-Wei Hong, Tzu-Yun Shann, Shih-Yang Su +2
Efficient exploration remains a challenging research problem in reinforcement learning, especially when an environment contains large state spaces, deceptive local optima, or spars…
Virtual-to-Real: Learning to Control in Visual Semantic Segmentation
Zhang-Wei Hong, Chen Yu-Ming, Shih-Yang Su +10
Collecting training data from the physical world is usually time-consuming and even dangerous for fragile robots, and thus, recent advances in robot learning advocate the use of si…
A Deep Policy Inference Q-Network for Multi-Agent Systems
Zhang-Wei Hong, Shih-Yang Su, Tzu-Yun Shann +2
We present DPIQN, a deep policy inference Q-network that targets multi-agent systems composed of controllable agents, collaborators, and opponents that interact with each other. We…