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Why do Policy Gradient Methods work so well in Cooperative MARL? Evidence from Policy Representation
July 10, 2022, 9 a.m. |
The Berkeley Artificial Intelligence Research Blog bair.berkeley.edu
In cooperative multi-agent reinforcement learning (MARL), due to its on-policy nature, policy gradient (PG) methods are typically believed to be less sample efficient than value decomposition (VD) methods, which are off-policy. However, some recent empirical studies demonstrate that with proper input representation and hyper-parameter tuning, multi-agent PG can achieve surprisingly strong performance compared to off-policy VD methods.
Why could PG methods work so well? In this post, we will present concrete analysis to show that in certain scenarios, e.g., …
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