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Next: Exponential Family PCA Up: Finding Approximate POMDP Solutions Previous: Partially Observable Markov Decision


Dimensionality Reduction

In order to find a low-dimensional representation of our beliefs, we will use statistical dimensionality reduction algorithms [CC94]. These algorithms search for a projection from our original high-dimensional representation of our beliefs to a lower-dimensional compact representation. That is, they search for a low-dimensional surface, embedded in the high-dimensional belief space, which passes near all of the sample beliefs. If we consider the evolution of beliefs from a POMDP as a trajectory inside the belief space, then our assumption is that trajectories for most large, real world POMDPs lie near a low-dimensional surface embedded in the belief space. Figure 6 depicts an example low-dimensional surface embedded in the belief space of the three-state POMDP described in the previous section.

Figure 6: A one-dimensional surface (black line) embedded in a two-dimensional belief space (gray triangle). Each black dot represents a single belief probability distribution experienced by the controller. The beliefs all lie near the low-dimensional surface.

Ideally, dimensionality reduction involves no information loss--all aspects of the data can be recovered equally well from the low-dimensional representation as from the high-dimensional one. In practice, though, we will see that we can use lossy representations of the belief (that is, representations that may not allow the original data or beliefs to be recovered without error) and still get good control. But, we will also see that finding such representations of probability distributions will require a careful trade-off between preserving important aspects of the distributions and using as few dimensions as possible. We can measure the quality of our representation by penalizing reconstruction errors with a loss function [CDS02]. The loss function provides a quantitative way to measure errors in representing the data, and different loss functions will result in different low-dimensional representations.

Principal Components Analysis

One of the most common forms of dimensionality reduction is Principal Components Analysis [Jol86]. Given a set of data, PCA finds the linear lower-dimensional representation of the data such that the variance of the reconstructed data is preserved. Intuitively, PCA finds a low-dimensional hyperplane such that, when we project our data onto the hyperplane, the variance of our data is changed as little as possible. A transformation that preserves variance seems appealing because it will maximally preserve our ability to distinguish between beliefs that are far apart in Euclidean norm. As we will see below, however, Euclidean norm is not the most appropriate way to measure distance between beliefs when our goal is to preserve the ability to choose good actions.

We first assume we have a data set of $ n$ beliefs $ \{b_1, \ldots,
b_n\} \in \mathbb{B}$, where each belief $ b_i$ is in $ \mathbb{B}$, the high-dimensional belief space. We write these beliefs as column vectors in a matrix $ B = [b_1 \vert \ldots \vert b_n]$, where $ B \in {\mathbb{R}}^
{\vert\mathcal{S}\vert \times n}$. We use PCA to compute a low-dimensional representation of the beliefs by factoring $ B$ into the matrices $ U$ and $ \tilde{B}$,

$\displaystyle B = U \tilde{B}^T.$ (1)

In equation (1), $ U\in{\mathbb{R}}^{\vert\mathcal{S}\vert \times l}$ corresponds to a matrix of bases that span the low-dimensional space of $ l < \vert\mathcal{S}\vert$ dimensions. $ \tilde{B} \in {\mathbb{R}}^{n \times l}$ represents the data in the low-dimensional space.2 From a geometric perspective, $ U$ comprises a set of bases that span a hyperplane $ \tilde{\mathbb{B}}$ in the high-dimensional space of $ \mathbb{B}$; $ \tilde{B}$ are the co-ordinates of the data on that hyperplane. If no hyperplane of dimensionality $ l$ exists that contains the data exactly, PCA will find the surface of the given dimensionality that best preserves the variance of the data, after projecting the data onto that hyperplane and then reconstructing it. Minimizing the change in variance between the original data $ B$ and its reconstruction $ U\tilde{B}^T$ is equivalent to minimizing the sum of squared error loss:

$\displaystyle L(B, U, \tilde{B}) = \Vert B - U\tilde{B}^T\Vert _F^2.$ (2)

PCA Performance

Figure 7 shows a toy problem that we can use to evaluate the success of PCA at finding low-dimensional representations. The abstract model has a two-dimensional state space: one dimension of position along one of two circular corridors, and one binary variable that determines which corridor we are in. States $ {s_1 \ldots
s_{100}}$ inclusive correspond to one corridor, and states $ {s_{101}
\ldots s_{200}}$ correspond to the other. The reward is at a known position that is different in each corridor; therefore, the agent needs to discover its corridor, move to the appropriate position, and declare it has arrived at the goal. When the goal is declared the system resets (regardless of whether the agent is actually at the goal). The agent has 4 actions: left, right, sense_corridor, and declare_goal. The observation and transition probabilities are given by discretized von Mises distributions [MJ00,SK02], an exponential family distribution defined over $ [-\pi:\pi)$. The von Mises distribution is a ``wrapped'' analog of a Gaussian; it accounts for the fact that the two ends of the corridor are connected. Because the sum of two von Mises variates is another von Mises variate, and because the product of two von Mises likelihoods is a scaled von Mises likelihood, we can guarantee that the true belief distribution is always a von Mises distribution over each corridor after each action and observation.

This instance of the problem consists of 200 states, with 4 actions and 102 observations. Actions 1 and 2 move the controller left and right (with some von Mises noise) and action 3 returns an observation that uniquely and correctly identifies which half of the maze the agent is in (the top half or the bottom half). Observations returned after actions 1 and 2 identify the current state modulo 100: the probability of each observation is a von Mises distribution with mean equal to the true state (modulo 100). That is, these observations indicate approximately where the agent is horizontally.

Figure 7: The toy maze of 200 states.

This maze is interesting because it is relatively large by POMDP standards (200 states) and contains a particular kind of uncertainty--the agent must use action 3 at some point to uniquely identify which half of the maze it is in; the remaining actions result in observations that contain no information about which corridor the agent is in. This problem is too large to be solved by conventional POMDP value iteration, but structured such that heuristic policies will also perform poorly.

Figure 8: Sample beliefs for the toy problem, from a sample set of 500, at different (non-contiguous) points in time. The left-most belief is the initial belief state.
\includegraphics[width=\picheight]{figs/} \includegraphics[width=\picheight]{figs/} \includegraphics[width=\picheight]{figs/} \includegraphics[width=\picheight]{figs/}

We collected a data set of 500 beliefs and assessed the performance of PCA on beliefs from this problem. The data were collected using a hand-coded controller, alternating at random between exploration actions and the MDP solution, taking as the current state the maximum-likelihood state of the belief. Figure 8 shows $ 4$ sample beliefs from this data set. Notice that each of these beliefs is essentially two discretized von Mises distributions with different weights, one for each half of the maze. The starting belief state is the left-most distribution in Figure 8: equal probability on the top and bottom corridors, and position along the corridor following a discretized von Mises distribution with concentration parameter 1.0 (meaning that $ p($state$ )$ falls to $ 1/e$ of its maximum value when we move $ 1/4$ of the way around the corridor from the most likely state).

Figure 9 examines the performance of PCA on representing the beliefs in this data set by computing the average error between the original beliefs $ B$ and their reconstructions $ U\tilde{B}$.3 In Figure 9(a) we see the average squared error (squared $ L_2$) compared to the number of bases, and in Figure 9(b), we see the average Kullbach-Leibler (KL) divergence. The KL divergence between a belief $ b$ and its reconstruction $ r =
U\tilde{b}$ from the low-dimensional representation $ \tilde{b}$ is given by

$\displaystyle KL(b   \Vert   r) = \sum_{i=1}^{\vert\mathcal{S}\vert} b(s_i) \ln \left ( \frac{b(s_i)}{r(s_i)} \right )$ (3)

Minimizing the squared $ L_2$ error is the explicit objective of PCA, but the KL divergence is a more appropriate measure of how much two probability distributions differ. 4

Figure 9: The average error between the original sample set $ B$ and the reconstructions $ U\tilde{B}$. (a) Squared $ L_2$ error, explicitly minimized by PCA, and (b) the KL divergence. The error bars represent the standard deviation from the mean of the error over the 500 beliefs.
[Average Squared L-2 Error]
[Average KL Divergence]

Unfortunately, PCA performs poorly at representing probability distributions. Despite the fact that probability distributions in the collected data set have only 3 degrees of freedom, the reconstruction error remains relatively high until somewhere between 10 and 15 basis functions. If we examine the reconstruction of a sample belief, we can see the kinds of errors that PCA is making. Figure 10 shows a sample belief (the solid line) and its reconstruction (the dotted line). Notice that the reconstructed belief has some strange artifacts: it contains ringing (multiple small modes), and also is negative in some regions. PCA is a purely geometric process; it has no notion of the original data as probability distributions, and is therefore free to generate reconstructions of the data that contain negative numbers or do not sum to 1.

Figure 10: An example belief and its reconstruction, using 10 bases.

Notice also that the PCA process is making its most significant errors in the low-probability regions of the belief. This is particularly unfortunate because real-world probability distributions tend to be characterized by compact masses of probability, surrounded by large regions of zero probability (e.g., Figure 4a). What we therefore need to do is modify PCA to ensure the reconstructions are probability distributions, and improve the representation of sparse probability distributions by reducing the errors made on low-probability events.

The question to be answered is what loss functions are available instead of the sum of squared errors, as in equation (2). We would like a loss function that better reflects the need to represent probability distributions.


... space.2
Many descriptions of PCA are based on a factorization $ USV^T$, with $ U$ and $ V$ column-orthonormal and $ S$ diagonal. We could enforce a similar constraint by identifying $ \tilde{B}=VS$; in this case the columns of $ U$ would have to be orthonormal while those of $ \tilde{B}$ would have to be orthogonal.
We use a popular implementation of PCA based on the Golub-Reinsche algorithm [GR70] available through the GNU Scientific Library [GDT+02].
... differ. 4
Note that before computing the KL divergence between the reconstruction and the original belief, we shift the reconstruction to be non-negative, and rescale it to sum to 1.

next up previous
Next: Exponential Family PCA Up: Finding Approximate POMDP Solutions Previous: Partially Observable Markov Decision
Nicholas Roy 2005-01-16