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The estimation of advantage is crucial for a number of reinforcement learningalgorithms, as it directly influences the choices of future paths. In thiswork, we propose a family of estimates based on the order statistics over thepath ensemble, which allows one to flexibly drive the learning process, towardsor against risks. On top of this formulation, we systematically study theimpacts of different methods for estimating advantages. Our findings revealthat biased estimates, when chosen appropriately, can result in significantbenefits. In particular, for the environments with sparse rewards, optimisticestimates would lead to more efficient exploration of the policy space; whilefor those where individual actions can have critical impacts, conservativeestimates are preferable. On various benchmarks, including MuJoCo continuouscontrol, Terrain locomotion, Atari games, and sparse-reward environments, theproposed biased estimation schemes consistently demonstrate improvement overmainstream methods, not only accelerating the learning process but alsoobtaining substantial performance gains.

Biased Estimates of Advantages over Path Ensembles