  In this paper, we investigate the issue of error accumulation in critic
networks updated via pessimistic temporal difference objectives. We show that
the critic approximation error can be approximated via a recursive fixed-point
model similar to that of the Bellman value. We use such recursive definition to
retrieve the conditions under which the pessimistic critic is unbiased.
Building on these insights, we propose Validation Pessimism Learning (VPL)
algorithm. VPL uses a small validation buffer to adjust the levels of pessimism
throughout the agent training, with the pessimism set such that the
approximation error of the critic targets is minimized. We investigate the
proposed approach on a variety of locomotion and manipulation tasks and report
improvements in sample efficiency and performance.
