We study multi-task reinforcement learning (RL) in tabular episodic Markov decision processes (MDPs).

The latent constructs are the lower-dimensional representations of these instruments.

We develop a novel method for carrying out model selection for Bayesian autoen-coders (BAEs) by means of prior hyper-parameter optimization.

Belanger & McCallum (2016) and Gygli et al. (2017) have shown that energy In this work, we propose Structured Energy As Loss (SEAL) to take advantage of the expressivity of energy networks without incurring the high inference cost. This raises a question: Can energy networks be used in a way that is as expressive as SPENs, as efficient at inference as feedforward approaches, and also easy to train?

Finding a good way to model probability densities is key to probabilistic inference.

As efficient exploration in BAMDPs hinges upon the judicious acquisition of information, our complexity measure highlights the worst-case difficulty of gathering information and exhausting epistemic uncertainty.

A.2 Proof of Theorem 1 To prove Theorem 1, we assume that G Proof of Lemma 1. Let's first rewrite Equation (4) as null null By Lemma 1, linearity of expectation and knowing that each RWT is independent from the other tours by the Strong Markov Property, Theorem 1 holds. MHM-GNN can recover edge-based models where representations don't use graph-wide However, on Rent the Runway we see the raw features achieving the highest performance. That is, structural information does not seem to be relevant to this specific task. All hyperparameters were chosen to minimize training loss. For k = 5, we used a minibatch of size 5 in all datasets.