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In standard reinforcement learning (RL), a learning agent seeks to optimize the overall reward. However, many key aspects of a desired behavior are more naturally expressed as constraints.

Inspired by the work by Ishida et al. (2023), we propose an estimator

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Whether itispossible tolearn intrinsic reward functions for learning agents remains an open problem.

Itthencomputesr log (A|s)| = e andupdates 11 and 12). We determined 6.1 Policy Wefirstwithout, in Letting T denote environment 21,22,.

A natural generalization is to replace this input setP of n points by a setP of n sets inX. The distance from such an input setP P to a setC of centers can then be defined as the distance between the closest point-center pair. This problem is calledk-mean for sets; see e.g.

A natural generalization is to replace this input setP of n points by a setP of n sets inX. The distance from such an input setP P to a setC of centers can then be defined as the distance between the closest point-center pair. This problem is calledk-mean for sets; see e.g.

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