text: 202,89,1480920576285892612,79918104,Chris Amato,"I'm excited about our new #AAAI2022 paper that seeks to better understand actor-critic methods for multi-agent RL. In particular, while state-based critics are popular, we show they have theoretical and empirical drawbacks that need to be considered.  <LINK>",https://arxiv.org/abs/2201.01221,"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. ","A Deeper Understanding of State-Based Critics in Multi-Agent