We also study the more general case of an additional entropy regularizer.

We provide a closed-form expression for the worst occupation measure.

However, they do not account for transition uncertainty, whereas learning robust policies can be computationally expensive.

We consider the problem of learning models for risk-sensitive reinforcement learning.

In such situations, the successor representation (SR) is a popular framework which supports rapid policy evaluation by decoupling a policy's

We first redefine notation for clarity and then provide the proofs of the results in the main paper. Now we first prove that the iteration in Eq.2 has a fixed point. Proof of Lemma 3.1: Let We present the bound on using empirical Bellman operator compared to the true Bellman operator. The proof can be found in [6]. Proof of Theorem 3.4: Recall that the expression of the V -function iterate is given by: Proof of Theorem 3.6: The proof of this statement is divided into two parts.

Model-free deepreinforcement learning (RL)algorithms havebeenwidely used for a range of complex control tasks.