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A Deeper Understanding of State-Based Critics in Multi-Agent Reinforcement Learning
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
Local Advantage Actor-Critic for Robust Multi-Agent Deep Reinforcement Learning
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
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning
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.
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning
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.
