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cs.LG2022
Robust Losses for Learning Value Functions
Andrew Patterson, Victor Liao, Martha White
Most value function learning algorithms in reinforcement learning are based on the mean squared (projected) Bellman error. However, squared errors are known to be sensitive to outl…
cs.LG2022
A Temporal-Difference Approach to Policy Gradient Estimation
Samuele Tosatto, Andrew Patterson, Martha White +1
The policy gradient theorem (Sutton et al., 2000) prescribes the usage of a cumulative discounted state distribution under the target policy to approximate the gradient. Most algor…