Using the loss function from equation (8) with the iterative optimization procedure described by equation (26) and equation (27) to find the low-dimensional factorization, we can look at how well this dimensionality-reduction procedure performs on some POMDP examples.
Recall from Figure 9 that we were unable to
find good representations of the data with fewer than 10 or 15 bases,
even though our domain knowledge indicated that the data had 3 degrees
of freedom (horizontal position of the mode along the corridor,
concentration about the mode, and probability of being in the top or
bottom corridor). Examining one of the sample beliefs in
Figure 10, we saw that the representation was worst
in the low-probability regions. We can now take the same data set from
the toy example, use E-PCA to find a low-dimensional representation
and compare the performance of PCA and E-PCA.
Figure 11(a) shows that E-PCA is substantially
more efficient at representing the data, as we see the KL divergence
falling very close to 0 after 4 bases. Additionally, the squared
error at 4 bases is
. (We need 4 bases for
perfect reconstruction, rather than 3, since we must include a
constant basis function. The small amount of reconstruction error
with 4 bases remains because we stopped the optimization procedure
before it fully converged.)
![]() [Reconstruction Performance] |
![]() [An Example Belief and Reconstruction] |
![]() [The Belief and Reconstruction Near the Peak] |
![]() [The Belief and Reconstruction In Low-Probability Region] |
Figure 11(b) shows the E-PCA reconstruction of the same example belief as in Figure 10. We see that many of the artifacts present in the PCA reconstruction are absent. Using only 3 bases, we see that the E-PCA reconstruction is already substantially better than PCA using 10 bases, although there are some small errors at the peaks (e.g., Figure 11c) of the two modes. (Using 4 bases, the E-PCA reconstruction is indistinguishable to the naked eye from the original belief.) This kind of accuracy for both 3 and 4 bases is typical for this data set.
Although the performance of E-PCA on finding good representations of the
abstract problem is compelling, we would ideally like to be able to use this
algorithm on real-world problems, such as the robot navigation problem in
Figure 2. Figures 12
and 13 show results from two such robot navigation
problems, performed using a physically-realistic simulation (although with
artificially limited sensing and dead-reckoning). We collected a sample set of
500 beliefs by moving the robot around the environment using a heuristic
controller, and computed the low-dimensional belief space
according to the algorithm in Table 1. The full
state space is
, discretized to a resolution of
per pixel, for a total of 799 states. Figure 12(a) shows
a sample belief, and Figure 12(b) the reconstruction using 5
bases. In Figure 12(c) we see the average
reconstruction performance of the E-PCA approach, measured as average
KL-divergence between the sample belief and its reconstruction. For
comparison, the performance of both PCA and E-PCA are plotted. The
E-PCA error falls
to
at 5 bases, suggesting that 5 bases are sufficient for good
reconstruction. This is a very substantial reduction, allowing us to represent
the beliefs in this problem using only 5 parameters, rather than 799
parameters. Notice that many of the states lie in regions that are ``outside''
the map; that is, states that can never receive probability mass were not
removed. While removing these states would be a trivial operation, the E-PCA
is correctly able to do so automatically.
![]() (a) Original Belief |
![]() (b) Reconstruction |
![]() (c) Reconstruction performance |
In Figure 13, similar results are shown for a different
environment. A sample set of 500 beliefs was again collected using a heuristic
controller, and the low-dimensional belief space was computed
using the E-PCA. The full state space is
, with a resolution
of
per pixel. An example belief is shown in
Figure 13(a), and its reconstruction using 6 bases is
shown Figure 13(b). The reconstruction performance as
measured by the average KL divergence is shown in
Figure 13(c); the error falls very close to 0 around 6
bases, with minimal improvement thereafter.
![]() [A sample belief] |
![]() [The reconstruction] |
![]() [Average reconstruction performance] |