The Mirage of Action-Dependent Baselines in Reinforcement Learning
ICML, pp. 5015-5024, 2018.
Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Several recent papers extend the baseline to depend on both the state and action and suggest that this significantly reduces variance and improves sample efficiency...More
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