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We consider the problem of finding a policy that maximizes an expected rewardthroughout the trajectory of an agent that interacts with an unknownenvironment. Frequently denoted Reinforcement Learning, this framework suffersfrom the need of large amount of samples in each step of the learning process.To this end, we introduce parameter critic, a formulation that allows samplesto keep their validity even when the parameters of the policy change. Inparticular, we propose the use of a function approximator to directly learn therelationship between the parameters and the expected cumulative reward. Throughconvergence analysis, we demonstrate the parameter critic outperformsgradient-free parameter space exploration techniques as it is robust to noise.Empirically, we show that our method solves the cartpole problem whichcorroborates our claim as the agent can successfully learn an optimal policywhile learning the relationship between the parameters and the cumulativereward.

Parameter Critic: a Model Free Variance Reduction Method Through Imperishable Samples