62 citations · 77 across the 5 of their papers we have counts for
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Semi-On-Policy Training for Sample Efficient Multi-Agent Policy Gradients
Bozhidar Vasilev, Tarun Gupta, Bei Peng +1
Policy gradient methods are an attractive approach to multi-agent reinforcement learning problems due to their convergence properties and robustness in partially observable scenari…
Regularized Softmax Deep Multi-Agent -Learning
Ling Pan, Tabish Rashid, Bei Peng +2
Tackling overestimation in -learning is an important problem that has been extensively studied in single-agent reinforcement learning, but has received comparatively little atte…
RODE: Learning Roles to Decompose Multi-Agent Tasks
Tonghan Wang, Tarun Gupta, Anuj Mahajan +3
Role-based learning holds the promise of achieving scalable multi-agent learning by decomposing complex tasks using roles. However, it is largely unclear how to efficiently discove…
Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
Tabish Rashid, Gregory Farquhar, Bei Peng +1
QMIX is a popular -learning algorithm for cooperative MARL in the centralised training and decentralised execution paradigm. In order to enable easy decentralisation, QMIX restr…
Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
Sanmit Narvekar, Bei Peng, Matteo Leonetti +3
Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. Despite many advances over th…
Optimistic Exploration even with a Pessimistic Initialisation
Tabish Rashid, Bei Peng, Wendelin Böhmer +1
Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL). In the tabular case, all provably efficient model-free algorithms rely…