3 citations · 13 across the 11 of their papers we have counts for
12 papers
One Demonstration Is Enough for Real-World Robotic Reinforcement Learning
Yuwan Liu, Hongze Yu, Song Liu +5
Learning effective robot control policies on physical hardware is challenging due to costly data collection and the difficulty of reward specification. Prior work has incorporated…
Rethinking Generalizability and Discriminability of Self-Supervised Learning from Evolutionary Game Theory Perspective
Jiangmeng Li, Zehua Zang, Qirui Ji +6
Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual…
Position: Foundation Agents as the Paradigm Shift for Decision Making
Xiaoqian Liu, Xingzhou Lou, Jianbin Jiao +1
Decision making demands intricate interplay between perception, memory, and reasoning to discern optimal policies. Conventional approaches to decision making face challenges relate…
Self-supervised Pretraining for Decision Foundation Model: Formulation, Pipeline and Challenges
Xiaoqian Liu, Jianbin Jiao, Junge Zhang
Decision-making is a dynamic process requiring perception, memory, and reasoning to make choices and find optimal policies. Traditional approaches to decision-making suffer from sa…
PDiT: Interleaving Perception and Decision-making Transformers for Deep Reinforcement Learning
Hangyu Mao, Rui Zhao, Ziyue Li +7
Designing better deep networks and better reinforcement learning (RL) algorithms are both important for deep RL. This work studies the former. Specifically, the Perception and Deci…
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement Learning
Jing Cui, Yufei Han, Yuzhe Ma +2
Backdoor attacks in reinforcement learning (RL) have previously employed intense attack strategies to ensure attack success. However, these methods suffer from high attack costs an…
Benchmarking Continual Learning from Cognitive Perspectives
Xiaoqian Liu, Junge Zhang, Mingyi Zhang +1
Continual learning addresses the problem of continuously acquiring and transferring knowledge without catastrophic forgetting of old concepts. While humans achieve continual learni…
ProAgent: Building Proactive Cooperative Agents with Large Language Models
Ceyao Zhang, Kaijie Yang, Siyi Hu +12
Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in the realm of multi-agent systems. Current approaches to developing cooperative agents rely…
Deep Reinforcement Learning with Task-Adaptive Retrieval via Hypernetwork
Yonggang Jin, Chenxu Wang, Tianyu Zheng +5
Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-ma…
DecisionHoldem: Safe Depth-Limited Solving With Diverse Opponents for Imperfect-Information Games
Qibin Zhou, Dongdong Bai, Junge Zhang +2
An imperfect-information game is a type of game with asymmetric information. It is more common in life than perfect-information game. Artificial intelligence (AI) in imperfect-info…
The Partially Observable Asynchronous Multi-Agent Cooperation Challenge
Meng Yao, Qiyue Yin, Jun Yang +5
Multi-agent reinforcement learning (MARL) has received increasing attention for its applications in various domains. Researchers have paid much attention on its partially observabl…
Coordinated Proximal Policy Optimization
Zifan Wu, Chao Yu, Deheng Ye +3
We present Coordinated Proximal Policy Optimization (CoPPO), an algorithm that extends the original Proximal Policy Optimization (PPO) to the multi-agent setting. The key idea lies…