Latent Matters: Learning Deep State-Space Models
Year: 2021

Abstract:
Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by maximising the evidence lower bound. However, as we show, this does not ensure the model actually learns the underlying dynamics. We therefore propose a constrained optimisation framework as a general approach for training DSSMs. Building upon this, we introduce the extended Kalman VAE (EKVAE), which combines amortised variational inference with classic Bayesian filtering/smoothing to model dynamics more accurately than RNN-based DSSMs. Our results show that the constrained optimisation framework significantly improves system identification and prediction accuracy on the example of established state-of-the-art DSSMs. The EKVAE outperforms previous models w.r.t. prediction accuracy, achieves remarkable results in identifying dynamical systems, and can furthermore successfully learn state-space representations where static and dynamic features are disentangled.

heading: 1 Introduction
text: Many dynamical systems can only be (partially) observed, with the exact dynamics unknown. Yet, precise models are needed for prediction and control [e.g. 19, 21, 25, 3]. Learning such accurate models is subject of current research, especially in image-based domains [e.g. 9].
Deep state-space models (DSSMs) [e.g. 28, 17, 13] describe sequence data by a (typically Markovian) nonlinear transition model and a nonlinear observation model. The transition model is assumed to capture the dynamics underlying the observed data, and the observation model maps the latent variables to the domain of observable data and accounts for the measurement noise. In this paper, we show that DSSMs, however, often do not learn the correct system dynamics, which is suboptimal for accurate predictions or performing downstream tasks, such as model-based reinforcement learning.
We identify in our experiments three main reasons causing this problem: (i) DSSMs are often trained by maximising the sequential evidence lower bound (ELBO). High ELBO values, however, do not imply the model has learned the correct system dynamics. (ii) The prior/initial distribution is usually just a Gaussian. This often leads to an over-regularisation of the approximate posterior or even to a broken generative model, where the transition model is not optimised to process samples from the prior. (iii) Most DSSMs use RNNs to approximate or support Bayesian filtering/smoothing. Yet, RNNs often prove to be a limiting factor for learning accurate models of the system dynamics; moreover, RNN-based transition models as in [8, 9] can lead to a non-Markovian state-space, where the latent variables do not capture the entire information about the system’s state.
To address these problems, we propose the following solutions: (i) we introduce a constrained optimisation (CO) framework as a general approach for learning DSSMs. It ensures a good reconstruction ∗Correspondence to a.klushyn@tum.de. †Work done while at Machine Learning Research Lab, Volkswagen Group, Munich.
35th Conference on Neural Information Processing Systems (NeurIPS 2021).
quality and thus provides a necessary basis for learning the underlying system dynamics. To this end, we extend a recent method [16] presented in the context of variational autoencoders (VAEs) to DSSMs. We do this by formulating the sequential ELBO as the Lagrangian of a CO problem and introducing the associated optimisation algorithm. (ii) We complement the proposed CO framework with a powerful empirical Bayes prior. (iii) To obtain more accurate predictions of observed dynamical systems, we introduce the extended Kalman VAE (EKVAE), where we dispense with RNNs by combining extended Kalman filtering/smoothing with amortised variational inference and a neural linearisation approach. Furthermore, we show that the EKVAE is capable of learning state-space representations where static and dynamic features are disentangled. We use this to validate the learned model in the context of model-based reinforcement learning.
Our evaluation includes experiments on the image data of a moving pendulum [13] and on the reacher environment [26], where we use angle as well as high-dimensional RGB image data as observations. We show that each of our proposed approaches significantly helps in learning accurate models of observed dynamical systems—and that applying our CO framework to established DSSMs leads to a substantial increase in their prediction accuracy.

heading: 2 Background: A Rate–Distortion Perspective on Deep State-Space Models
text: DSSMs [e.g. 28, 17, 13] model an unknown distribution of observed sequence data x1:T = (x1,x2, . . . ,xT ) by means of typically lower-dimensional latent variables z1:T that represent the underlying state of the system. To achieve this, the Markov assumption is imposed. It states that the future state zt+1 as well as the current observation xt solely depend on zt:
p(x1:T , z1:T |u1:T ) = pθ(x1:T |z1:T ) pψ(z1:T |u1:T )
= p(z1) pθ(x1| z1) T∏ t=2 pψ(zt| zt−1,ut−1) pθ(xt| zt), (1)
where u1:T are optional control signals (actions), and the use of different parameters (θ, ψ) will become important in the course of this paper. The model parameters in Eq. (1) are often learned through amortised variational inference [e.g. 17, 13]. This requires introducing a recognition model qφ(z1:T |x1:T ,u1:T ) that learns—in combination with the transition model pψ(zt| zt−1,ut−1)—the dynamics underlying the observed data. The resulting objective function is known as sequential ELBO:
log p(x1:T |u1:T ) ≥ FELBO(θ, ψ, φ) = Eqφ [ log
pθ(x1:T | z1:T ) pψ(z1:T |u1:T ) qφ(z1:T |x1:T ,u1:T )
] . (2)
In the context of generative models, the ELBO can be divided into a reconstruction term (distortion) and a compression term (rate) [1]. We extend the theory in [1] to DSSMs, where the distortion D(θ, φ) = −Eqφ [ log pθ(x1:T | z1:T ) ] optimises the model’s ability for reconstructing observations,
whereas the rateR(φ, ψ) = KL ( qφ(z1:T |x1:T ,u1:T )‖ pψ(z1:T |u1:T ) ) enables learning the underlying dynamics. These definitions lead to the following general formulation of the ELBO:
FELBO(θ, ψ, φ) = −D(θ, φ)−R(ψ, φ). (3) Balancing the ratio between distortion and rate during optimisation can be an effective approach to improve the learning of DSSMs, as we discuss in the following section.

heading: 3 Constrained Optimisation Framework for Improved System Identification
text: High ELBO values do not necessarily imply that the model has learned the underlying system dynamics of the observed data, as we verify in Sec. 6.2. This is because different combinations of rate and distortion can result in the same ELBO value. Previous work addresses this issue by introducing weighting schedules for either D(θ, φ) orR(ψ, φ) [e.g. 4] since a different ratio favours either better reconstruction or compression [1]. However, we demonstrate in Sec. 6 that balancing reconstruction and compression with predefined annealing schedules often does not achieve the desired result.
A more recent approach originates from the framework of VAEs: Rezende and Viola [23] and Klushyn et al. [16] define the VAE as a CO problem allowing for controlling the model’s reconstruction quality.
We transfer this approach to DSSMs to ensure a good reconstruction—i.e. a low D(θ, φ)—and thus provide a sufficient basis for learning the underlying system dynamics. To this end, we formulate the sequential ELBO as the Lagrangian of a CO problem (i) by specifying the rate R(ψ, φ) in Eq. (3) as optimisation objective; and (ii) by imposing the inequality constraint D(θ, φ) ≤ D0. Here, D0 is a hyperparameter that defines the baseline for our desired reconstruction quality—we provide a heuristic for the simple determination of D0 in App. A.1. The resulting Lagrangian is
L(θ, ψ, φ;λ) = R(ψ, φ) + λ ( D(θ, φ)−D0 ) , (4)
where the Lagrange multiplier λ can be viewed as a weighting term for the distortion.
The original EM algorithm [e.g. 20] for optimising the ELBO, minθ,ψ minφ −FELBO(θ, ψ, φ), provides the following connection to the CO problem:
M-step︷︸︸︷ min θ,ψ E-step︷ ︸︸ ︷ max λ min φ L(θ, ψ, φ;λ) s.t. λ ≥ 0, (5)
where, unlike in the original EM algorithm, we want qφ(z1:T |x1:T ,u1:T ) to additionally satisfy the inequality constraint D(θ, φ) ≤ D0 in the E-step. However, as detailed in [16], it can only be guaranteed that L(θ, ψ, φ;λ) optimises a lower bound on log p(x1:T |u1:T ) if and only if 1 ≥ λ ≥ 0.

heading: 3.1 Learning the Initial Distribution
text: It is common practice to define the initial/prior distribution p(z1) as a standard normal [e.g. 17, 8]. However, in this case, the prior KL in the ELBO can cause an over-regularisation (cf. [16]) of the approximate posterior and thus of the transition model. Furthermore, if the discrepancy between prior and posterior is too large, we may obtain a broken generative model, where p(zt| zt−1,ut−1) is not trained to process samples from p(z1). We provide empirical evidence in Sec. 6.1.
This issue often arises in the context of neural models trained by stochastic gradient methods: in order to alleviate possible vanishing-gradient problems, time-series data is typically cut into equally-sized short-length units. For sufficiently large datasets, the initial states can therefore be assumed to cover most possible states, which results in a nontrivial marginal approximate posterior EpD [ qφ(z1|x,u) ] .
Since the optimal prior distribution is p∗(z1) = EpD [ qφ(z1|x,u) ] [cf. 27], an empirical Bayes prior pψ0(z1) must have the complexity to approximate pψ0(z1) ≈ p∗(z1). For this reason, we propose to learn a hierarchical prior pψ0(z1) = ∫ pψ0(z1| ζ) p(ζ) dζ as part of the DSSM by applying the variational approach in [16]. Klushyn et al. [16] define, by means of an approximate distribution qφ0(ζ|z1), a (VAE-like) lower bound on the optimal empirical Bayes prior:
Ep∗(z1) [ log pψ0(z1) ] ≥ EpD(x1:T ,u1:T ) Eqφ(z1:T |x1:T ,u1:T ) [ Eqφ0 (ζ| z1) [ log
pψ0(z1| ζ) p(ζ) qφ0(ζ| z1) ] ︸ ︷︷ ︸
=FVHP(ψ0,φ0; z1)
] , (6)
where p(ζ) is a standard normal distribution and pD(x1:T ,u1:T ) is the empirical distribution of our data. This method is referred to as variational hierarchical prior (VHP). Learning the VHP as part of the model is consistent with the CO problem since Eq. (6) introduces an upper bound on the rate, R(ψ, φ, ψ0) ≤ R(ψ, φ, ψ0, φ0) (see App. A.2), and thus a lower bound on the ELBO. As a result, we obtain the Lagrangian L(θ, ψ, φ, ψ0, φ0;λ) = R(ψ, φ, ψ0, φ0) + λ ( D(θ, φ)−D0 ) and arrive at the following optimisation problem:
Empirical Bayes︷︸︸︷ min ψ0,φ0 M-step︷︸︸︷ min θ,ψ E-step︷ ︸︸ ︷ max λ min φ L(θ, ψ, φ, ψ0, φ0;λ) s.t. λ ≥ 0. (7)

heading: 3.2 Optimisation Algorithm
text: In order to find the saddle point of the Lagrangian in Eq. (7), we propose Alg. 1, an extension of REWO [16] to DSSMs. Alg. 1 ensures that we optimise a lower bound on log p(x1:T |u1:T ) at the end of training, which is the case for 1 ≥ λ ≥ 0. For this purpose, we apply a special update scheme
for λ, introduced and explained in depth in [16]: λ(i) = λ(i−1) · exp [ −ν · fλ ( λ(i−1), D(i)(θ, φ)−D0; τ1, τ2 ) · ( D(i)(θ, φ)−D0 )] . (8)
In this context, i denotes the iteration step of the optimisation process. The function fλ is defined as fλ(λ, δ; τ1, τ2) = ( 1−H(δ) ) · tanh (τ1 · (1/λ− 1))− τ2 ·H (δ), where H is the Heaviside function, and τ1 as well as τ2 are slope parameters.
Furthermore, Alg. 1 allows us to efficiently learn the parameters of the VHP (ψ0, φ0) and the transition model (ψ) by dividing the CO process into two phases: an initial and a main phase, which is the reason why we use different parameters for the transition and observation model. In the initial phase, the model is optimised w.r.t. (θ, φ) to reduce the reconstruction error by learning the features of the individual observations xt. The main phase starts as soon as the inequality constraintD(θ, φ) ≤ D0 is satisfied. This serves as starting point for additionally optimising the parameters of the VHP (ψ0, φ0) and the transition model (ψ), i.e. for learning the system dynamics.
Algorithm 1 REWO for deep state-space models Initialise i = 1 Initialise λ(0) = 1 Initialise INITIALPHASE = TRUE while training do
Compute D̂ba (batch average) D̂(i) = (1− α) · D̂ba + α · D̂(i−1), (D̂(0) = D̂ba) λ(i) ← λ(i−1) · exp [ −ν · fλ ( λ(i−1), D̂(i) −D0; τ1, τ2 ) · ( D̂(i) −D0 )] if D̂(i) ≤ D0 then
INITIALPHASE = FALSE end if if INITIALPHASE then
Optimise L(θ, ψ, φ, ψ0, φ0;λ(i)) w.r.t. θ, φ else
Optimise L(θ, ψ, φ, ψ0, φ0;λ(i)) w.r.t. θ, ψ, φ, ψ0, φ0 end if i← i+ 1
end while

heading: 4 Extended Kalman VAE
text: The CO framework can be applied to any DSSM whose objective function is covered by the general rate–distortion formulation of the ELBO (Eq. (3)). We provide derivations for popular baseline models [17, 13] in App. A.3 and A.4. However, to achieve high prediction accuracies, the model itself should not prove to be a limiting factor for learning a precise description of the system dynamics.
Most DSSMs use deterministic RNNs as part of the recognition and/or transition model [e.g. 17, 13, 8, 9]. In [17], for instance, the parameters of the approximate posterior are learned through a (bidirectional) RNN, which is expected to replace classic Bayesian filtering/smoothing. The RNNbased transition model in [e.g. 8], on the other hand, allows combining amortised variational inference with Kalman filtering/smoothing [12, 22]. However, the use of RNNs often leads to less accurate models of the dynamical system, as we show in Sec. 6.2 and 6.3.
In order to increase the prediction accuracy, we introduce the extended Kalman VAE (EKVAE), where we dispense with RNNs by combining amortised variational inference with Bayesian filtering/smoothing. To compute the posterior, we leverage the concept of extended Kalman filters/smoothers [e.g. 11] but avoid the computationally expensive linearisation (Taylor expansion) of the transition and observation model. We achieve this (i) by directly learning the Jacobian of the dynamic model as a function of the current state, which we refer to as neural linearisation (Sec. 4.1); and (ii) by introducing a linear auxiliary-variable model similar to [8, 18] (Sec. 4.2). The EKVAE can be used as filter or smoother. In the following, we focus on the more complex smoother version—as it allows learning a more precise model [2]—and refer to App. A.6 for the filter version.

heading: 4.1 Neural Linearisation of the Dynamic Model
text: We model the nonlinear dynamical system by a Gaussian transition model that is locally linear w.r.t. discrete time steps [28, 13]:
pψ(zt+1| zt,ut) = N ( zt+1|Fψ(zt,ut) zt +Bψ(zt,ut)ut, Qψ(zt,ut) ) , (9)
where Fψ , Bψ , and Qψ are modelled by linear combinations of M weighted base matrices:
Fψ(zt,ut) = M∑ m=1 α (m) ψ (zt,ut)F (m) etc., where αψ(zt,ut) = softmax ( gψ(zt,ut) ) ∈ RM.
(10) The base matrices { F(m),B(m),Q(m) }M m=1
are learned parameters, and gψ(z,u) is implemented as a neural network.
Next, we make the connection to extended Kalman filtering/smoothing, where the prediction step is based on the local Jacobian (first-order Taylor expansion) of the nonlinear transition function zt+1 = f(zt,ut) + qt, which is unknown in our case. Instead of computing the local Jacobian, however, our transition model (Eq. (9)) is designed to globally find the best linearisation at each time step as a function of the current state and action, which we refer to as neural linearisation.
This approach allows us to apply the extended Kalman filter or smoother algorithm, but replace the computationally expensive Taylor expansion in the prediction step with Fψ(zt,ut) and Bψ(zt,ut), as we derive in App. A.5 and verify in our experiments.

heading: 4.2 Linear Auxiliary-Variable Model
text: The observation model often needs to learn highly nonlinear functions, especially in case of highdimensional sensory data, such as images. In order to enable an analytic computation of the posterior but avoid an expensive linearisation of the observation model, we introduce auxiliary variables a1:T with a linear dependence on z1:T . As in [8, 18], we learn the nonlinear mapping from a1:T to the high-dimensional observations x1:T by a VAE’s encoder–decoder pair, qφ(at|xt) and pθ(xt|at). Since the dynamics are modelled by the transition function in z1:T—and at can be viewed as a low-dimensional representation of xt—we obtain the following observation model:
p(x1:T ,a1:T | z1:T ) = T∏ t=1 pθ(xt|at) pψ(at| zt). (11)
In contrast to [8, 18], we propose a time-invariant auxiliary-variable model,
pψ(at| zt) = N (at|Hzt, R), (12)
where H and R are globally learned or predefined. The time-invariant H additionally allows us to learn disentangled state-space representations, as we elaborate at the end of this section.
By using a1:T as pseudo observations, our update step corresponds to the classical Kalman filter/smoother algorithm because we do not need to linearise the observation model (see App. A.5). In combination with the neural linearisation approach, we can now analytically compute the filtered and smoothed distributions, pψ(zt|a1:t,u1:t−1) and pψ(zt|a1:T ,u1:T−1), respectively. Note, however, that these are generally not optimal because we have a nonlinear Gaussian system that is locally linearised. As a result, we obtain the recognition model (smoother version):
q(z1:T ,a1:T |x1:T ,u1:T ) = T∏ t=1 pψ(zt|a1:T ,u1:T−1) T∏ t=1 qφ(at|xt). (13)
Disentangling Static and Dynamic Features In the context of latent-variable models, disentanglement typically means that different features are represented by different dimensions in latent space [e.g. 10]. DSSMs learn a representation of the system’s state in the latent space. It can usually be split into static and dynamic features, e.g. the position and velocity of a robot arm, where the position can be inferred from a single frame, while the velocity requires a sequence of frames.
The EKVAE can be used to disentangle static and dynamic features due to its architecture: static features are represented separately by the auxiliary variables at, which are learned via the encoder– decoder pair. By defining H in pψ(at| zt) (Eq. (12)) as rectangular identity matrix (cf. Fig. 5),
H = (δij) ∈ RDa×Dz , (14)
the model learns a latent representation where the first Da dimensions of zt correspond to static features at ∈ RDa , such that z(d)t = a (d) t for d = 1, 2, . . . , Da. The remaining Dz −Da dimensions of zt represent dynamic features, as we verify in Sec. 6.4.

heading: 4.3 Integration With the CO Framework: Deriving Distortion and Rate
text: To integrate the EKVAE with the CO framework introduced in Sec. 3, we define in the following the distortion D(θ, φ) and rateR(ψ, φ, ψ0, φ0) based on the transition, observation, and recognition model in Eqs. (9, 11, 13)—and the VHP in Eq. (6). A detailed derivation is provided in App. A.6.
The distortion is simply defined by the encoder–decoder pair:
D(θ, φ) = − T∑ t=1 Eqφ(at|xt) [ log pθ(xt|at) ] . (15)
Deriving the rate is more complicated: (i) we need to perform a sample-based optimisation of transition parameters ψ. This is especially crucial for gψ(zt,ut) in Eq. (10), where an optimisation solely via extended Kalman smoothing, i.e. via deterministic mean values (cf. App. A.5), does not cover the range of application and would therefore result in a poorly trained transition model. (ii) Our recognition model q(z1:T ,a1:T |x1:T ,u1:T ) (cf. Eq. (13)) does not contain the computationally more expensive pairwise smoothed distributions pψ(zt, zt−1|a1:T ,u1:T−1) but only smoothed distributions pψ(zt|a1:T ,u1:T−1). However, an optimisation of pψ(zt| zt−1,ut−1) based on samples (zt, zt−1) from smoothed distributions would lead to an inaccurate transition model.
To address these issues, we use the rate Eqφ(a1:T |x1:T ) [∑T t=1 log qφ(at|xt)
pψ(at| a1:t−1,u1:t−1)
] as our starting
point. But instead of computing pψ(at|a1:t−1,u1:t−1) analytically, we solve the corresponding integral (derived from the Bayesian filtering equations, see App. A.6.2) only w.r.t. zt in closed form and marginalise zt−1 via Monte Carlo integration:
log pψ(at|a1:t−1,u1:t−1) = log ∫∫ pψ(at| zt)pψ(zt| zt−1,ut−1)pψ(zt−1|a1:t−1,u1:t−2)dztdzt−1
≥ Epψ(zt−1| a1:T ,u1:T−1) [ log
pψ(at| zt−1,ut−1) pψ(zt−1|a1:t−1,u1:t−2) pψ(zt−1|a1:T ,u1:T−1)
] . (16)
In this context, the distribution pψ(at| zt−1,ut−1) plays a crucial role as it includes all transition parameters ψ and decouples zt from zt−1. It therefore allows a sample-based optimisation of the transition model on the basis of the smoothed distribution pψ(zt−1|a1:T ,u1:T−1). As a result—the complete derivation can be found in App. A.6.2—we obtain the following rate (smoother version):
R(ψ, φ, ψ0, φ0) = Eq(z1:T ,a1:T |x1:T ,u1:T ) [ Rinitial(ψ, φ, ψ0, φ0;a1:T , z1)
+ T∑ t=2 ( log qφ(at|xt) pψ(at| zt−1,ut−1) + log pψ(zt−1|a1:T ,u1:T−1) pψ(zt−1|a1:t−1,u1:t−2) )] , (17)
where the empirical Bayes prior introduced in Sec. 3.1 is learned via
Rinitial(ψ, φ, ψ0, φ0;a1:T , z1) = log qφ(a1|x1) pψ(a1| z1) + log pψ(z1|a1:T ,u1:T−1)−FVHP(ψ0, φ0; z1).
(18)
Note that the log distributions in Eq. (17) and (18) can be expressed as closed-form KL divergences (see App. A.6.2). The distortion (Eq. (15)) and rate (Eq. (17)) now allow us to define the Lagrangian of the CO problem in Eq. (7) and thus to integrate the EKVAE with our CO framework.

heading: 5 Related Work
text: A popular method for avoiding local optima when maximising the ELBO is referred to as annealing [e.g. 4]. Here, the rate is multiplied by a weighting term β that is increased from 0 to 1 during training. However, such predefined schedules often prove to be suboptimal, as we show in Tab. 1. For this reason, we extend the VAE-based approach in [16] to DSSMs by deriving a general Lagrangian formulation of the sequential ELBO on the basis of distortion and rate. This allows to represent the above weighting term by a Lagrange multiplier λ = 1/β, which is updated based on the reconstruction quality. Our proposed optimisation algorithm builds on [16] and includes several modifications to facilitate learning the underlying system dynamics, as we detail in Sec. 3.2.
Many DSSMs [e.g. 17, 8] use simple Gaussian prior/initial distributions, resulting in less accurate transition models (cf. Sec. 6.2 and App. A.7.2). In the empirical Bayes approach of [13], a separate recognition model learns an initial pseudo state ζ ∼ p(ζ|x1:T ,u1:T ), which is then mapped to z1 = f(ζ) through a neural network. By contrast, the VAE-like empirical Bayes method (VHP) [16] that we use in our CO framework can directly substitute a Gaussian p(z1) without further restrictions on the model architecture (cf. Sec. 3.1), which moreover leads to better results (see Tab. 1).
Since the introduction of stochastic gradient variational Bayes [15, 24], various extensions have been proposed for learning DSSMs via amortised variational inference, where, in contrast to the EKVAE (ours), classic Bayesian filtering/smoothing is approximated/replaced by deterministic RNNs [17, 13, 9, 5, 7, 29, 6]. Two popular examples—which we evaluate and integrate in our CO framework—are deep Kalman filters/smoothers (DKF/DKS) [17] and deep variational Bayes filters/smoothers (DVBF/DVBS) [13, 14]. Krishnan et al. [17] define two different recognition models based on uni-/bidirectional RNNs that parametrise the approximate filtered/smoothed distribution. In [13, 14], the approximate posterior is obtained by sharing parameters between the recognition and transition model; and an RNN is used for the initial time step, as described above. Although, DVBF/DVBS uses the same locally-linear transition model as the EKVAE, it does not take advantage of closed-form Bayesian inference, leading to a less accurate dynamic model, as we verify in Tab. 1.
Previous work has shown that hidden states of RNNs [8, 21] or probabilistic switch variables [18] can be used to predict the parameters of a (time-inhomogeneous) linear SSM in order to enable closedform Bayesian inference. The Kalman VAE (KVAE) [8], for example, uses an auxiliary-variable model p(x1:T ,a1:T , z1:T |u1:T ) = p(x1:T |a1:T ) p(a1:T | z1:T ) p(z1:T |u1:T ) and is based on linear Gaussian p(at| zt,ht−1) and p(zt| zt−1,ht−1,ut−1), whose model parameters are conditioned on a deterministic hidden state ht−1 = LSTM(a1:t−1) for modelling nonlinear dynamics. This allows analytically computing the posterior by Kalman filtering/smoothing. In the EKVAE, we use a similar auxiliary-variable model as in [8], but dispense with RNNs/switch variables by choosing a transition model with a nonlinear dependence on zt−1. To compute the posterior, we leverage the concept of extended Kalman filtering/smoothing (cf. Sec. 4). This is beneficial as, for example, the LSTM-based deterministic path in the transition of [8] leads to a less accurate dynamic model and a non-Markovian state-space, meaning that not all information about the state is encoded in zt (see Sec. 6.3).

heading: 6 Experiments
text: We validate our approach on image data of a moving pendulum and on the reacher environment of Deepmind’s control suite [26], where we use angle as well as high-dimensional image data. The pendulum dataset was originally introduced in [13] and consists of 500 sequences with 15 images each, which have a size of 16× 16 pixels. The reacher dataset consists of 2000 sequences with 30 time steps each. We use two versions in our experiments: (i) partially observed system states, i.e. the angles of the first and second joint; and (ii) RGB images of 64× 64 pixels in size. In our experiments, we use smoothing posteriors. This leads to more precise models [cf. 2] and allows inferring an accurate state-space representation of partially observed systems already in the initial time step. In Sec. 6.1, we demonstrate our CO framework on the example of deep Kalman smoothers (DKSs, see App. A.3) [17]; and show in Sec. 6.2 that it significantly improves learning the system dynamics on the example of DKSs, deep variational Bayes smoothers (DVBSs, see App. A.4) [13, 14], and EKVAEs (ours). Furthermore, we verify in Sec. 6.3 that RNN-based transition models, as in KVAEs [8] and RSSMs [9], lead to a non-Markovian state-space. In Sec. 6.4, we show the benefits of disentangled state-space representations for model-based reinforcement learning.

heading: 6.1 Demonstrating the CO Framework on the Example of DKS
text: Karl et al. [13] have shown that DKS is not capable of learning the angular velocity of the pendulum, i.e. to accurately predict the system, when trained classically or with annealing. In the following, we show that our CO framework solves this problem. We refer to the resulting model as VHP-DKS (CO), implying that it is trained via CO with the VHP as part of the model (see App. A.3 for the derivation).
Fig. 1 shows the optimisation process of VHP-DKS (CO): the model learns the underlying system dynamics, which is indicated by the barrel shape of the state-space representation [cf. 28, 13] and verified in Sec. 6.2. Complementary to this, we demonstrate in Fig. 2 that the VHP significantly improves the quality of generated sequences (no broken generative model) by learning a prior that matches EpD [ q(z1|x1:T ,u1:T ) ] —and show that annealing [4], in contrast to our CO framework, does not enable DKS to infer the angular velocity, which confirms the experimental findings in [13].

heading: 6.2 The Influence of the Learned State-Space Representation on the Prediction Accuracy
text: High ELBO values do not imply the model can accurately predict the observed system, as we show in Tab. 1. Our CO framework solves this problem: it improves system identification, leading to a significant increase in the prediction accuracy of the models (note the impact of the VHP). We evaluate if a system has been identified based on the correlation between inferred and ground-truth states—i.e. rotation angles and angular velocities—which is measured by R2 of an OLS regression [cf. 13]. The prediction accuracy is evaluated by the MSE of 500 predicted sequences (pendulum/reacher: 15/30 times steps), conditioned on { z (n) 1 ∼ q(z1|x (n) 1:5 ,u (n) 1:4 ) }500 n=1
. This allows us to additionally verify the quality of the learned state-space representation in the initial time step. The EKVAE outperforms DKS and DVBS w.r.t. prediction accuracy and is even capable of identifying the dynamical system of reacher on the basis of 64× 64 pixels RGB images (see Fig. 3 and Tab. 1c).
Supplementary to Tab. 1, we provide in App. A.7.2 (i) a statistic evaluation of different annealing schedules compared with CO, which is based on 25 runs each; (ii) visualisations of the state-space representations (initial time step and entire sequence) learned by the different models; and (iii) further evaluations including reconstructed, predicted, and generated sequences.

heading: 6.3 Comparison With the KVAE: Limitations of RNN-Based Transition Models
text: RNN-based transition models can lead to a non-Markovian state space, i.e. not all information about the state is encoded in zt, but partially in the RNN. This can significantly restrict (Bayesian) filtering and smoothing, resulting in a lower prediction accuracy of the model, as we show in Tab. 2 and Fig. 4.
The KVAE uses the transition model p(zt+1| zt,ht,ut), where ht=LSTM(a1:t) (see Sec. 5). As shown in Tab. 2, this leads to a lower prediction accuracy compared to the EKVAE (cf. Tab. 1a); and predictions conditioned on the smoothed {z1 ∼ p(z1|a1:5,u1:4), h1} are significantly less accurate than predictions conditioned on the filtered {z5 ∼ p(z5|a1:5,u1:4), h5}, which we explain in Fig. 4 and App. A.7.3. Note that the same applies to the RSSM, as we detail in App. A.7.3.

heading: 6.4 Encoding Rewards: Policy Learning With Disentangled State-Space Representations
text: The EKVAE can learn state-space representations where static and dynamic features are disentangled (see Sec. 4.2). In the context of model-based reinforcement learning, these are often position and velocity, as we demonstrate on the example of pendulum (Fig. 5) and reacher (Fig. 3).
Such a disentangled representation allows us to use observations for encoding a goal position pg = a through q(a|x) or a goal velocity vg through p(z|a1:T ,u1:T−1) q(a1:T |x1:T ), where vg ∈ RDv is represented by the last Dv = Dz −Da dimensions of z (cf. Sec. 4.2). Therefore, if rewards are not available, the EKVAE can be used for defining reward functions based on an encoded pg or vg , that target dimensions in zt either representing the position or the velocity: rpost (zt,pg) = − ∑Da d=1 ( z (d) t − p (d) g )2 or rvelt (zt,vg) = − ∑Dv d=1 ( z (Da+d) t − v (d) g )2 , where the negative mean squared error is a natural choice motivated by the Euclidean distance metric [cf. 16]. Depending on the task, we can use either rpost (zt,pg) or r vel t (zt,vg) to learn a policy πω(ut| zt) by
maximising J(ω) = ∑H−1 t=1 Eπω(ut| zt) Ep(zt+1| zt,ut) [ rt+1 ] , where H is the planning horizon.
In Fig. 6 and 7, we use the above method to validate the EKVAE in the context of model-based reinforcement learning. Our results show that the EKVAE allows learning accurate policies without having access to (external) rewards. We provide experimental details and further results in App. A.7.4.

heading: 7 Conclusion
text: In this paper, we have dealt with the question of how to learn DSSMs to obtain accurate predictions of observed dynamical systems. We have addressed the learning problem by proposing a CO framework for generic DSSMs. To this end, we have derived a general Lagrangian formulation of the sequential ELBO on the basis of distortion and rate—and extended the empirical Bayes prior (VHP) and the associated optimisation algorithm introduced in the context of VAEs to DSSMs. Building upon the CO framework, we have introduced the EKVAE, which combines extended Kalman filtering/smoothing with amortised variational inference and a neural linearisation approach.
Our experimental evaluations have demonstrated that applying the proposed CO framework to established DSSMs (e.g. DKF/DKS and DVBF/DVBS) facilitates system identification, with the VHP avoiding over-regularisation and broken generative models. The result is a substantial increase in prediction accuracy. In this context, we have shown that the EKVAE achieves a significantly higher prediction accuracy than state-of-the-art (RNN-based) models. Furthermore, we have shown that the EKVAE can learn disentangled position–velocity representations and demonstrated how these can be used for model-based reinforcement learning to define/encode reward functions and learn policies.

heading: Acknowledgements
text: We would like to thank Maximilian Karl and Djalel Benbouzid for valuable feedback and discussions.
Funding Transparency Statement
None of the authors received related third party funding or third party support during the 36 months prior to the submission of this work. None of the authors had financial relationships with entities that could potentially be perceived to influence the submitted work in the 36 months prior to submission.

Review summaries:
The authors make three contributions. 1) They apply the constrained optimization framework for VAE type models to the deep state space model case. 2) They add in the hierarchical prior approach to model the initial distribution for the first state. 3) They propose a model with linear transition matrices given by a neural network weighting learned basis matrices. Auxiliary observation variables are also introduced to allow for a linear observation model along with an extra encoder/decoder. This means filtering and smoothing distributions can be computed analytically.

This paper has two main contributions.  1. The authors adapt REWO for helping with disentanglement of learned representations and improving predictive accuracy 2. The authors replace the RNN encoding of the nonlinearity in [6] by the locally linear model of [11-12] while keeping closed form inference  The authors then illustrate how combining these together leads to better accuracy in learned latent dynamics for a set of models, in particular they show that their approach can recover the unobserved velocity as a latent variable in motion models.

This paper proposes a framework for learning neural network-based state-space models from raw data. In comparison to the standard RNN-based approach [1], there are several modifications: (i) a modified ELBO formulated in the context of a constrained optimization (CO) framework [2]. CO prioritizes reconstruction term at the beginning of the training; (ii) hierarchical prior [3] instead of a simple Gaussian; (iii) a modified Kalman-VAE framework [4] instead of RNN-based transition model. Experimental evidence on pendulum data and the reacher environment indicates that the resulting framework outperforms baselines in predictive modeling and ELBO values.   [1] A Recurrent Latent Variable Model for Sequential Data  [2] Taming VAEs  [3] Learning Hierarchical Priors in VAEs  [4]  A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning

This paper proposes various improvements to deep sequential latent variable models (or deep state space models). Namely, 1) the constrained optimization method from the Taming VAEs paper [Rezende & Viola, 2018], 2) a method for improving empirical priors over the first time step’s latent variable (referred to as VHP), and 3) a new model that combines the locally linear mappings of Karl et al., 2017 and the KVAE variable separation from Fraccaro et al., 2017. These improvements are demonstrated on pendulum and reacher datasets (both with images and joint angles), showing improvements in system identification, prediction accuracy, and the ability to use the state representations from MBRL. Overall, the paper provides several helpful tips for constructing and training deep state space models.