Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics ofobservedsequence data.

Time series forecasting plays akeyrole in informing industrial and business decisions [17,24,8], while segmentation isuseful forunderstanding biological andphysicalsystems [40,45,34].

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Papandreou and Yuille[4] proposed the Perturb-and-MAP Random Field, an efficient sampling method forgeneral MarkovRandom Field. We compare the detailed structure of gradients of each estimator. All gradients are formed as a summation over the steps. The Gumbel-CRF and PM-MRF estimator can be decomposed with a pathwise term, where we take gradientoff w.r.t. Since the official test set is not publically available, we use the same training/ validation/ test split as Fu et al.[1].

Most existing work in this area relies on non-probabilistic approaches or on complex Reinforcement Learning (RL)-based hard structures.

Skill learning offers one way of identifying these regularities by decomposing pre-collected experiences intoasequence ofskills.

For all datasets, the same model and training hyper-parameters (except learning rate) were used.

Recall thatx = argmina Ax>θ so x can be viewed as a deterministic functionθ . " log p(zn|θ) (1/|Nε|) P Since Rmax is the upper bound of maximum expected reward, the second term can be bounded 2Rmaxγ. We letΦ R|A| d as the feature matrix where each row ofΦrepresent each action inA. We summarize the procedure of estimating t,It inAlgorithm3. LetX denote the feasible set.

However,extrinsic rewards may be insufficiently informative to encourage an agent to explore and understand its environment, particularly in partially observed settings where the agent has a limited view of its environment.

We randomly selected action sequences from test episodes collected with action noise alongside the training episodes. Next, we analyze the IDM framework based on Janner's work [1]. Denote pθ(z |z,a) as the state transition probability predicted by model.