The constraint setX Rd1 is compact and convex.

A reinforcement learning (RL) agent is tasked with two fundamental, interdependent problems: exploration(howtodiscoverusefuldata),andcreditassignment(howtoincorporateit). The simplest way of estimating the value function is by averaging returns (futurediscountedsumsofrewards)startingfromtaking ainx.

Giventextual explanations, our proposed framework uses agenerative model conditioned on textual input to create data points representing the explanations. Bycomparing theneuron'sresponse tothese generated data points and control data points, we can estimate the quality of the explanation.