no code implementations • 3 Jan 2022 • Xueguang Lyu, Andrea Baisero, Yuchen Xiao, Christopher Amato
Centralized Training for Decentralized Execution, where training is done in a centralized offline fashion, has become a popular solution paradigm in Multi-Agent Reinforcement Learning.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 16 Oct 2021 • Yuchen Xiao, Xueguang Lyu, Christopher Amato
By using this local critic, each agent calculates a baseline to reduce variance on its policy gradient estimation, which results in an expected advantage action-value over other agents' choices that implicitly improves credit assignment.
Multi-agent Reinforcement Learning Policy Gradient Methods +2
no code implementations • 8 Feb 2021 • Xueguang Lyu, Yuchen Xiao, Brett Daley, Christopher Amato
Centralized Training for Decentralized Execution, where agents are trained offline using centralized information but execute in a decentralized manner online, has gained popularity in the multi-agent reinforcement learning community.
no code implementations • 15 Dec 2018 • Xueguang Lyu, Christopher Amato
When multiple agents learn in a decentralized manner, the environment appears non-stationary from the perspective of an individual agent due to the exploration and learning of the other agents.