StaQ, a finite-memory Policy Mirror Descent algorithm, converges to the optimal entropy-regularized policy with a sufficiently large window of past Q-functions and performs competitively with baselines.
For PQN, we use the CleanRL implementation (Huang et al., 2022)
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StaQ it! Growing neural networks for Policy Mirror Descent
StaQ, a finite-memory Policy Mirror Descent algorithm, converges to the optimal entropy-regularized policy with a sufficiently large window of past Q-functions and performs competitively with baselines.