Instead, we apply powerful machine learning models for one-class (Moya et al., 1993) or

We provide both theoretical analysis and experimental results to validate the effectiveness of our proposed algorithm.

The original MuZero did not use sticky actions (Machado et al., 2017) (a 25% chance that the selected action is ignored and that instead the previous action is repeated) for Atari experiments. For all experiments in this work we used a network architecture based on the one introduced by MuZero(Schrittwieser etal.,2020), To implement the network, we used the modules provided by the Haiku neural network library (Henniganetal.,2020). We did not observe any benefit from using a Gaussian mixture, so instead inallourexperiments weusedasingle Gaussian withdiagonal covariance. All experiments used the Adam optimiser (Kingma & Ba, 2015) with decoupled weight decay (Loshchilov & Hutter, 2017) for training.

This paper identifies astructural property of data distributions that enables deep neural networks to learn hierarchically.

Misalignedrewards can lead to suboptimal behaviors [Ngo et al., 2022], undermining the potential benefits of RL in practical scenarios.

Theorem D.2.Assumetheconditionsin Lemma D.1 and Assumption 4.1 hold. Proof.DenoterandommatrixXi = (si,ai) (si,ai)> .

Methods for inference and simulation of linearly constrained Gaussian Markov Random Fields (GMRF) are computationally prohibitive when the number of constraintsislarge.