2 citations · 4 across the 6 of their papers we have counts for
6 papers
Mastering the Game of Go with Self-play Experience Replay
Jingbin Liu, Xuechun Wang
The game of Go has long served as a benchmark for artificial intelligence, demanding sophisticated strategic reasoning and long-term planning. Previous approaches such as AlphaGo a…
STAG-VIO: Stabilized Prompt-to-Geometry Interface for Robust Dynamic Visual--Inertial Odometry
Rui Zhou, Jingbin Liu, Junbin Xie +3
Dynamic visual-inertial odometry (VIO) requires reliable suppression of motion-corrupted measurements, yet prior semantic-assisted approaches depend on category-limited segmenters…
Mastering Asymmetrical Multiplayer Game with Multi-Agent Asymmetric-Evolution Reinforcement Learning
Chenglu Sun, Yichi Zhang, Yu Zhang +4
Asymmetrical multiplayer (AMP) game is a popular game genre which involves multiple types of agents competing or collaborating with each other in the game. It is difficult to train…
Soft Q Network
Jingbin Liu, Shuai Liu, Xinyang Gu
Deep Q Network (DQN) is a very successful algorithm, yet the inherent problem of reinforcement learning, i.e. the exploit-explore balance, remains. In this work, we introduce entro…
Policy Optimization Reinforcement Learning with Entropy Regularization
Jingbin Liu, Xinyang Gu, Shuai Liu
Entropy regularization is an important idea in reinforcement learning, with great success in recent algorithms like Soft Q Network (SQN) and Soft Actor-Critic (SAC1). In this work,…
Reinforcement learning with world model
Jingbin Liu, Xinyang Gu, Shuai Liu
Nowadays, model-free reinforcement learning algorithms have achieved remarkable performance on many decision making and control tasks, but high sample complexity and low sample eff…