no code implementations • ICLR 2019 • Honghua Dong, Jiayuan Mao, Xinyue Cui, Lihong Li
In this paper, we advocate the use of explicit memory for efficient exploration in reinforcement learning.
1 code implementation • 25 Sep 2023 • Yangjun Ruan, Honghua Dong, Andrew Wang, Silviu Pitis, Yongchao Zhou, Jimmy Ba, Yann Dubois, Chris J. Maddison, Tatsunori Hashimoto
Alongside the emulator, we develop an LM-based automatic safety evaluator that examines agent failures and quantifies associated risks.
3 code implementations • 8 Jul 2020 • Yuhuai Wu, Honghua Dong, Roger Grosse, Jimmy Ba
In this work, we focus on an analogical reasoning task that contains rich compositional structures, Raven's Progressive Matrices (RPM).
2 code implementations • ICLR 2019 • Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou
We propose the Neural Logic Machine (NLM), a neural-symbolic architecture for both inductive learning and logic reasoning.
no code implementations • ICLR 2018 • Jiayuan Mao, Honghua Dong, Joseph J. Lim
Recent state-of-the-art reinforcement learning algorithms are trained under the goal of excelling in one specific task.
Hierarchical Reinforcement Learning reinforcement-learning +1