25 citations · 60 across the 13 of their papers we have counts for
13 papers
Provably Efficient Adversarial Imitation Learning with Unknown Transitions
Tian Xu, Ziniu Li, Yang Yu +1
Imitation learning (IL) has proven to be an effective method for learning good policies from expert demonstrations. Adversarial imitation learning (AIL), a subset of IL methods, is…
On Efficient Reinforcement Learning for Full-length Game of StarCraft II
Ruo-Ze Liu, Zhen-Jia Pang, Zhou-Yu Meng +3
StarCraft II (SC2) poses a grand challenge for reinforcement learning (RL), of which the main difficulties include huge state space, varying action space, and a long time horizon.…
Offline Reinforcement Learning with Causal Structured World Models
Zheng-Mao Zhu, Xiong-Hui Chen, Hong-Long Tian +2
Model-based methods have recently shown promising for offline reinforcement learning (RL), aiming to learn good policies from historical data without interacting with the environme…
Neural-to-Tree Policy Distillation with Policy Improvement Criterion
Zhao-Hua Li, Yang Yu, Yingfeng Chen +3
While deep reinforcement learning has achieved promising results in challenging decision-making tasks, the main bones of its success --- deep neural networks are mostly black-boxes…
An Introduction of mini-AlphaStar
Ruo-Ze Liu, Wenhai Wang, Yanjie Shen +3
StarCraft II (SC2) is a real-time strategy game in which players produce and control multiple units to fight against opponent's units. Due to its difficulties, such as huge state s…
NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
Rongjun Qin, Songyi Gao, Xingyuan Zhang +5
Offline reinforcement learning (RL) aims at learning a good policy from a batch of collected data, without extra interactions with the environment during training. However, current…