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Neural Replicator Dynamics
[article]
2020
arXiv
pre-print
Policy gradient and actor-critic algorithms form the basis of many commonly used training techniques in deep reinforcement learning. Using these algorithms in multiagent environments poses problems such as nonstationarity and instability. In this paper, we first demonstrate that standard softmax-based policy gradient can be prone to poor performance in the presence of even the most benign nonstationarity. By contrast, it is known that the replicator dynamics, a well-studied model from
arXiv:1906.00190v5
fatcat:2ym5g7kb2zfatawsu7xq76l5ga