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Found 3 papers, 1 papers with code
Marginal Likelihood Gradient for Bayesian Neural Networks
Bayesian learning of neural networks is attractive as it can protecting against over-fitting and provide automatic methods for inferring important hyperparameters by maximizing the marginal probability of the data.
Compatible features for Monotonic Policy Improvement
In this work we establish conditions under which the parametric approximation of the critic does not introduce bias to the updates of surrogate objective.
Policy Optimization Through Approximate Importance Sampling
These proxy objectives allow stable and low variance policy learning, but require small policy updates to ensure that the proxy objective remains an accurate approximation of the target policy value.
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