327 citations · 783 across the 13 of their papers we have counts for
8 papers · 1 filter
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents
Kaiqing Zhang, Zhuoran Yang, Han Liu +2
Despite the increasing interest in multi-agent reinforcement learning (MARL) in multiple communities, understanding its theoretical foundation has long been recognized as a challen…
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Jiechao Xiong, Qing Wang, Zhuoran Yang +7
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we…
Fully Implicit Online Learning
Chaobing Song, Ji Liu, Han Liu +2
Regularized online learning is widely used in machine learning applications. In online learning, performing exact minimization ( implicit update) is known to be beneficial t…
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Peng Sun, Xinghai Sun, Lei Han +8
Starcraft II (SC2) is widely considered as the most challenging Real Time Strategy (RTS) game. The underlying challenges include a large observation space, a huge (continuous and i…
Factorized Q-Learning for Large-Scale Multi-Agent Systems
Ming Zhou, Yong Chen, Ying Wen +5
Deep Q-learning has achieved significant success in single-agent decision making tasks. However, it is challenging to extend Q-learning to large-scale multi-agent scenarios, due to…
Curse of Heterogeneity: Computational Barriers in Sparse Mixture Models and Phase Retrieval
Jianqing Fan, Han Liu, Zhaoran Wang +1
We study the fundamental tradeoffs between statistical accuracy and computational tractability in the analysis of high dimensional heterogeneous data. As examples, we study sparse…
Feedback-Based Tree Search for Reinforcement Learning
Daniel R. Jiang, Emmanuel Ekwedike, Han Liu
Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a model-based reinforcement learning (RL)…
Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents
Kaiqing Zhang, Zhuoran Yang, Han Liu +2
We consider the problem of \emph{fully decentralized} multi-agent reinforcement learning (MARL), where the agents are located at the nodes of a time-varying communication network.…