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Bei Peng — most-cited papers & profile · Large Language Models
← authors
·
overview
Bei Peng
20
papers ·
591
citations ·
12
h-index
University of Liverpool · Shaanxi University of Chinese Medicine
Google Scholar ↗
Semantic Scholar ↗
OpenAlex ↗
Most-cited papers
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
2020 · 228 citations
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
2020 · 114 citations
FACMAC: Factored Multi-Agent Centralised Policy Gradients
2020 · 106 citations
Regularized Softmax Deep Multi-Agent $Q$-Learning
2021 · 16 citations
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
2020 · 15 citations
Optimistic Exploration even with a Pessimistic Initialisation
2020 · 13 citations
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
2020 · 7 citations
Accelerating Laboratory Automation Through Robot Skill Learning For Sample Scraping
2022 · 5 citations
Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
2021 · 1 citations
How Does the Lagrangian Guide Safe Reinforcement Learning through Diffusion Models?
2026
Heuristic Transformer: Belief Augmented In-Context Reinforcement Learning
2025
MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures
2025
DUAL: Dynamic Uncertainty-aware Learning
2025
SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments
2021
Centralised rehearsal of decentralised cooperation: Multi-agent reinforcement learning for the scalable coordination of residential energy flexibility
2023
Topics
Multi-Agent
Value-Based
Game AI
Policy Gradient
Exploration
cs.LG
Meta-RL
cs.SY
eess.SY
cs.AI