METREE: Max-Entropy Exploration with Random Encoding for Efficient RL with Human Preferences

Isabel Y.N Guan, Xin Liu, Dingyuan Zhang, Estella Zhao, Zhenzhong Jia*

10.1109/ROBIO58561.2023.10355039

2023-12-04

International Conference on Robotics and Biomimetics

979-8-3503-2571-3

2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)

1-8

2023-12-4--2023-12-9

泰国

In recent years, reinforcement learning has achieved significant advances in practical domains such as robotics. However, conveying intricate objectives to agents in reinforcement learning (RL) remains challenging, often necessitating detailed reward function design. In this study, we introduce an innovative approach, MEETRE, which integrates max-entropy exploration strategies with random encoders. This offers a streamlined and efficient solution for human-involved preference-based RL without the need for meticulously designed reward functions. Furthermore, MEETRE sidesteps the need for additional models or representation learning, leveraging the power of randomly initialized encoders for effective exploration.

Representation learning Biomimetics Biological system modeling Reinforcement learning Encoding Robots

其他

EI

20240315404335

人工提交

南方科技大学

Isabel Y.N Guan, Xin Liu, Dingyuan Zhang, Estella Zhao, Zhenzhong Jia*. METREE: Max-Entropy Exploration with Random Encoding for Efficient RL with Human Preferences[C],2023:1-8.