Brief instructions to install and run symbolicPerseus
-----------------------------------------------------
Author: Pascal Poupart (ppoupart@cs.uwaterloo.ca)
Reference: Chapter 5 of Poupart's PhD thesis
(http://www.cs.uwaterloo.ca/~ppoupart/publications/ut-thesis/ut-thesis.pdf)
Installation:
-------------
1) Untar and unzip symbolicPerseus.tgz
>> tar -xvzf symbolicPerseus.tgz
2) Compile the java code with Sun's java SDK. Use the same version as
Matlab's java virtual machine. To check the java version used by
matlab, type "version -java" at the Matlab prompt. The java code has
been compiled with java 1.5.0 without any problem.
>> cd symbolicPerseus/javaClasses
>> javac *.java
Launching Matlab
----------------
Launch matlab in the symbolicPerseus directory. This is critical
since matlab will automatically read the files startup.m and
java.opts which tell Matlab's java virtual machine where to find the
java classes (startup.m) and how much memory to allocate
(java.opts).
>> cd symbolicPerseus
>> matlab
N.B. Do not launch matlab with the -nojvm option because that will
prevent the java virtual machine from starting up. The code has been
tested with Matlab 7.3.
Solving POMDPs:
---------------
Run the "solvePOMDP" function by typing
>> [valueFunction,policy] = solvePOMDP('problems/coffee3po.txt');
It first parses the POMDP 'coffee3po.txt' in the "explicit SPUDD format".
The explicit SPUDD format requires that all values of all variables be
specified (i.e., no shortcut allowed). Then some reachable belief
states are sampled by following a some initial policy (default policy is
QMDP with look ahead depth of 1). This may take a few
minutes. Then the POMDP is solved by running the boundedPerseus.m
function. Again, this may take a few minutes per iteration. The
value function and policies are automatically saved in the file
'problems/coffee3po_Lbel_Miter_Nsize.mat' indicating that L belief states
were sampled, M Perseus iterations were performed and at most N alpha vectors
were computed at each iteration.
There are several parameters that can be passed to the "solvePOMDP"
function to fine tune the computation. Type "help solvePOMDP" for a
summary of these parameters and their default values.
Evaluating a policy:
--------------------
To evaluate a policy, run one of the evalPOMDPpolicy functions by typing
>> avRew = evalPOMDPpolicyGraph('problems/coffee3po.txt', 'problems/coffee3po_Lbel_Miter_Nsize.mat')
or
>> avRew = evalPOMDPpolicyStationary('problems/coffee3po.txt', 'problems/coffee3po_Lbel_Miter_Nsize.mat')
or
>> avRew = evalPOMDPpolicyNonStationary('problems/coffee3po.txt', 'problems/coffee3po_Lbel_Miter_Nsize.mat')
It averages the total discounted reward earned over 500 runs of 50 steps
each. The average of the runs performed so far is reported after each run.
Note that evalPOMDPpolicyGraph is the fastest function. It selects actions
based on the policy graph that is computed by symbolic perseus. The value
of the initial belief state reported by symbolic perseus is an estimate of
the policy graph. evalPOMDPpolicyNonStationary requires a lot of memory but
executes a policy that is strictly better than the policy graph. Actions
are selected by finding the best alpha vector computed at each iteration of
symbolic perseus. evalPOMDPpolicyStationary selects actions by finding
the best alpha vector of the last iteration of symbolic perseus. Hence
only the value function of the last iteration needs to be loaded in memory,
which reduces memory requirements. However, the policy may have a value
arbitrarily lower than that of the non-stationary policy or the
policy graph.
Executing a stationary policy:
------------------------------
1) Parse a POMDP file
>> ddPOMDP = parsePOMDP('problems/coffee3po.txt');
2) Load a policy and value function
>> load problems/coffee3po_Lbel_Miter_Nsize.mat;
3) Query a policy
>> [actId,actName] = queryPolicy(belState, valueFunction, policy, ddPOMDP)
The argument "ddPOMDP" comes from parsing the POMDP file in step 1.
The arguments "valueFunction" and "policy" are obtained in step 2 when
loading a policy file. The argument "belState" can be set to
ddPOMDP.initialBelState (initial belief state) the first time or to the
updated belief state computed in step 4.
4) Update a belief state
>> nextBelState = beliefUpdate(ddPOMDP, belState, actId, obsConfig);
The argument "ddPOMDP" comes from parsing the POMDP file in step 1.
The argument "belState" is the current belief state. The argument
"actId" is the action selected in step 3. The argument "obsConfig" is
an observation. ObsConfig should be a 2xN matrix such that the first
row lists the ids of the observation variables and the second row
lists the values of the observation variables. For example [4,5;1,2]
indicates that observation variable 4 has value 1 and observation
variable 5 has value 2. Note that the ids of the observation variables
should be those of the "primed" version (i.e., ids of the variables at the
next time step). You can find out the ids of the observation
variables by typing:
>> [ddPOMDP.obsVars.id]
and the primed observation variables by typing:
>> [ddPOMDP.obsVars.id] + ddPOMDP.nVars