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Found 3 papers, 1 papers with code
Refining Minimax Regret for Unsupervised Environment Design
In this work, we introduce Bayesian level-perfect MMR (BLP), a refinement of the minimax regret objective that overcomes this limitation.
Bayesian Exploration Networks
Empirical results demonstrate that BEN can learn true Bayes-optimal policies in tasks where existing model-free approaches fail.
Why Target Networks Stabilise Temporal Difference Methods
Integral to recent successes in deep reinforcement learning has been a class of temporal difference methods that use infrequently updated target values for policy evaluation in a Markov Decision Process.
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