137 citations · 412 across the 61 of their papers we have counts for
11 papers · 1 filter
MA2CL:Masked Attentive Contrastive Learning for Multi-Agent Reinforcement Learning
Haolin Song, Mingxiao Feng, Wengang Zhou +1
Recent approaches have utilized self-supervised auxiliary tasks as representation learning to improve the performance and sample efficiency of vision-based reinforcement learning a…
Unified 2D and 3D Pre-Training of Molecular Representations
Jinhua Zhu, Yingce Xia, Lijun Wu +5
Molecular representation learning has attracted much attention recently. A molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds, and can also be represent…
Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods
Qing Li, Wengang Zhou, Zhenbo Lu +1
Actor-critic Reinforcement Learning (RL) algorithms have achieved impressive performance in continuous control tasks. However, they still suffer two nontrivial obstacles, i.e., low…
LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning
Mingyu Yang, Jian Zhao, Xunhan Hu +3
Cooperative multi-agent reinforcement learning (MARL) has made prominent progress in recent years. For training efficiency and scalability, most of the MARL algorithms make all age…
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed Agents
Jian Zhao, Youpeng Zhao, Weixun Wang +5
Multi-agent reinforcement learning is difficult to be applied in practice, which is partially due to the gap between the simulated and real-world scenarios. One reason for the gap…
MCMARL: Parameterizing Value Function via Mixture of Categorical Distributions for Multi-Agent Reinforcement Learning
Jian Zhao, Mingyu Yang, Youpeng Zhao +4
In cooperative multi-agent tasks, a team of agents jointly interact with an environment by taking actions, receiving a team reward and observing the next state. During the interact…
Learning Robust Policy against Disturbance in Transition Dynamics via State-Conservative Policy Optimization
Yufei Kuang, Miao Lu, Jie Wang +3
Deep reinforcement learning algorithms can perform poorly in real-world tasks due to the discrepancy between source and target environments. This discrepancy is commonly viewed as…
Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic
Zhihai Wang, Jie Wang, Qi Zhou +2
Model-based reinforcement learning algorithms, which aim to learn a model of the environment to make decisions, are more sample efficient than their model-free counterparts. The sa…
Supervised Off-Policy Ranking
Yue Jin, Yue Zhang, Tao Qin +4
Off-policy evaluation (OPE) is to evaluate a target policy with data generated by other policies. Most previous OPE methods focus on precisely estimating the true performance of a…
A Generalization Theory based on Independent and Task-Identically Distributed Assumption
Guanhua Zheng, Jitao Sang, Houqiang Li +2
Existing generalization theories analyze the generalization performance mainly based on the model complexity and training process. The ignorance of the task properties, which resul…
Deep Model-Based Reinforcement Learning via Estimated Uncertainty and Conservative Policy Optimization
Qi Zhou, Houqiang Li, Jie Wang
Model-based reinforcement learning algorithms tend to achieve higher sample efficiency than model-free methods. However, due to the inevitable errors of learned models, model-based…