Introduction

The Resource Allocating Vector Quantizer (RAVQ) was developed by Fredrik Linaker and Lars Niklasson. Given a data set of vectors, the RAVQ generates a set of model vectors that are meant to represent typical categories of vectors within the data set. Resource allocating in this case means that the number of categories is not fixed, but is dynamically determined as the unsupervised learning proceeds.

The RAVQ consists of three main parts:

• Input buffer
• Moving average of vectors in the input buffer
• Set of model vectors

There are three key RAVQ parameters that must be set before learning begins. Each of these parameters will affect the number of categories that will be created.

• Buffer size
  This value determines the number of input vectors that are stored in the buffer. The size should reflect the probable rate of change within the environment. A small buffer size may lead to spurious categories that are based on noise. A large buffer size will cause the moving average to be quite stable and may lead to very few categories.
• Epsilon
  This value determines how close the moving average must be to the input buffer vectors to qualify as a possible new model vector. A small epsilon means that the vectors that make up the moving average must be nearly identical in order to be considered as a potential category. A large epsilon means that the vectors that make up the moving average could be quite dissimilar and still be considered as a potential category.
• Delta This value determines how different a moving average vector must be from all existing model vectors to justify creating a new model vector. A small delta means that the moving average need not be very different from existing model vectors in order to create a new category. A large delta means that the moving average must be significantly different from the existing model vectors to create a new category.

In this lab we will observe a robot running a basic wander and avoid brain. On each time step it will check its sensors and create a vector representing the minimum sonar values on its left, front-left, front, front-right, and right. It will pass this vector of sensor values to a RAVQ. The RAVQ will be trying to learn appropriate categories for this environment and will report any time the current category changes. After a period of learning, the robot will stop and print out the current set of RAVQ categories. Then we will analyze the categories it found, trying to describe them verbally. Next we will reposition the robot back to the starting point and observe its behavior again. We will also try modifying the RAVQ parameters to gain a better understanding of how these settings affect the types of categories that are formed.

Getting Started

1. To get copies of the files you'll need for this lab, open a terminal window and type:

   update-cogs1
   

2. To move down to the appropriate directory, type:

   cd cogs1/labs/6
   

3. To start the categorization process, type:

   python basicRAVQ.py &
   

   This will open up a pyrobot window. Before you begin, use the mouse to grab the lower right corner of the pyrobot window and drag it down to make it bigger. Then press the Run button. This will run the robot for 200 steps. It will print each step number and in addition show you when the current category found by the RAVQ has changed. At the end of 200 steps it will print out all of the RAVQ's current categories.
4. Press the Stop button. For each category generated by the RAVQ, write down a verbal description of what it represents. Remember that the RAVQ is using 5 sonar sensor values: left, front-left, front, front-right, and right. Also recall that sonar sensors reflect distances to an obstacle. So small values indicate that an obstacle is quite close and large values indicate that there is open space in front of that particular sensor.
5. Move the robot back to its approximate starting position. Be sure to set its heading to be towards the top of the screen. At the command line of the pyrobot window type:

   self.counter = 0
   

   This will reset the counter. Now press the Run button again. Whenever a new category is reported, stop the robot and compare it's current location to your verbal description of that category. Do they seem to coincide?
6. Now let's try to modify the parameter settings and see how the number of categories changes. Click on the brain's filename in the pyrobot window basicRAVQ.py. This will bring up an edit window containing the program. At the top of the file you'll see several lines like the following:

   self.bufferSize = 5
   self.epsilon = 1.0
   self.delta = 0.3
   

   Change the parameter settings here. Then save the file in the edit window. Go back to the pyrobot window and press the Reload Brain button. Then press the Run button to see the results. Try buffer sizes of 1 and 20. Try epsilons of 0.1 and 4.0. Try deltas of 0.1 and 0.9. Only change one parameter at a time, keeping the others at their initial values. Record the number of model vectors created in each case.
7. Based on your findings from experimenting with the parameters, change all three parameter settings to try to get the most possible model vectors.

What to turn in for this lab

1. Using the original parameter settings, train the RAVQ for 200 time steps. Write down a summary of each model vector. Then reset the robot back to its approximate starting position and reset the counter back to 0. Re-run the robot and report on whether the categories it encounters on this run match your descriptions. Does it find additional categories on the second run? If so, in what ways are the different from the existing categories?
2. Summarize the results of your experiments with the parameter settings. Did the description of the parameters at the top of this page match your findings? What setting of the parameters led to the most model vectors being created?
3. In this lab we've seen that we can use a RAVQ to categorize the various states that a non-adaptive robot encounters as it moves around its world. However, we'd ultimately like to use these categories in an adaptive robot. Discuss how a categorization method, like the RAVQ, might be useful to a learning robot. Don't worry about the details of any specific learning algorithm, just think about the big picture.