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Title: 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. Many such methods take the form of actor-critic with state-based critics, since centralized training allows access to the true system state, which can be useful during training despite not being available at execution time. State-based critics have become a common empirical choice, albeit one which has had limited theoretical justification or analysis. In this paper, we show that state-based critics can introduce bias in the policy gradient estimates, potentially undermining the asymptotic guarantees of the algorithm. We also show that, even if the state-based critics do not introduce any bias, they can still result in a larger gradient variance, contrary to the common intuition. Finally, we show the effects of the theories in practice by comparing different forms of centralized critics on a wide range of common benchmarks, and detail how various environmental properties are related to the effectiveness of different types of critics.  more » « less
Award ID(s):
2044993 1816382
NSF-PAR ID:
10315765
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the AAAI Conference on Artificial Intelligence
ISSN:
2374-3468
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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