  Policy gradient algorithms have proven to be successful in diverse decision
making and control tasks. However, these methods suffer from high sample
complexity and instability issues. In this paper, we address these challenges
by providing a different approach for training the critic in the actor-critic
framework. Our work builds on recent studies indicating that traditional
actor-critic algorithms do not succeed in fitting the true value function,
calling for the need to identify a better objective for the critic. In our
method, the critic uses a new state-value (resp. state-action-value) function
approximation that learns the value of the states (resp. state-action pairs)
relative to their mean value rather than the absolute value as in conventional
actor-critic. We prove the theoretical consistency of the new gradient
estimator and observe dramatic empirical improvement across a variety of
continuous control tasks and algorithms. Furthermore, we validate our method in
tasks with sparse rewards, where we provide experimental evidence and
theoretical insights.
