public void QLearningGridPathTest()
{
var random = new Random(1337);
var gridSize = 16;
var grid = new int[gridSize, gridSize];
var ql = new QLearning(gridSize * gridSize, 4, new EpsilonGreedyExplorationPolicy(0.0), 0.3, 0.8);
var pathLength = 20;
var maxReward = 0.0;
for (var x = 0; x < gridSize; x++)
{
for (var y = 0; y < gridSize; y++)
{
grid[x, y] = random.Next(0, 100);
}
}
// train
for (var i = 0; i < 100; i++)
{
var x = random.Next(0, gridSize);
var y = random.Next(0, gridSize);
var currentReward = 0.0;
ql.Begin(x + y * gridSize);
for (var j = 0; j < pathLength; j++)
{
switch ((Action)ql.SelectedAction)
{
case Action.UP:
y++;
break;
case Action.DOWN:
y--;
break;
case Action.LEFT:
x--;
break;
case Action.RIGHT:
x++;
break;
}
var r = 0;
if (x < 0 || x >= gridSize || y < 0 || y >= gridSize)
{
x = x < 0 ? 0 : x >= gridSize ? gridSize - 1 : x;
y = y < 0 ? 0 : y >= gridSize ? gridSize - 1 : y;
}
else
{
r = grid[x, y];
}
currentReward += r;
var nextState = x + y * gridSize;
ql.Step(r, nextState);
}
if (currentReward > maxReward)
{
maxReward = currentReward;
}
}
Assert.AreEqual(896, maxReward);
}
