  We introduce Probabilistic Actor-Critic (PAC), a novel reinforcement learning
algorithm with improved continuous control performance thanks to its ability to
mitigate the exploration-exploitation trade-off. PAC achieves this by
seamlessly integrating stochastic policies and critics, creating a dynamic
synergy between the estimation of critic uncertainty and actor training. The
key contribution of our PAC algorithm is that it explicitly models and infers
epistemic uncertainty in the critic through Probably Approximately
Correct-Bayesian (PAC-Bayes) analysis. This incorporation of critic uncertainty
enables PAC to adapt its exploration strategy as it learns, guiding the actor's
decision-making process. PAC compares favorably against fixed or pre-scheduled
exploration schemes of the prior art. The synergy between stochastic policies
and critics, guided by PAC-Bayes analysis, represents a fundamental step
towards a more adaptive and effective exploration strategy in deep
reinforcement learning. We report empirical evaluations demonstrating PAC's
enhanced stability and improved performance over the state of the art in
diverse continuous control problems.
