def initializeQLearningObject(self, rlParams):
self.qLearn = QLearning.QLearning(self.numFeatures, self.features,
self.numActions, 1024, 0.50, 32, 2,
0.95, 0.20, 0.95, self)
self.nextState = State.State(
self.numFeatures
) # this will hold the next state object for the bass2 layer
# [' ','#', 1,'#', 10],
# ['S',' ',' ',' ',' '],
# [-10,-10, -10, -10, -10]]
# grid = [[' ',' ',' ',+1],
# ['#','#',' ','#'],
# [' ','#',' ',' '],
# [' ','#','#',' '],
# ['S',' ',' ',' ']]
grid = [[ '#',-100, -100, -100, -100, -100, '#'],
[ 1, 'S', ' ', ' ', ' ', ' ', 10],
[ '#',-100, -100, -100, -100, -100, '#']]
Grid = GridWorld(grid, 0.01, -1.0)
M = MarkovDecisonProcess(0.4, Grid, 100)
M.ValueIteration()
values = M.getValues()
actions = M.getActions()
qvalues = M.getQValues()
for i in values.keys():
print(f"{i} : {values[i]}, {actions[i]}")
RL = QLearning(Grid, 50000, alpha = 0.6, epsilon = 0.2, discount = 0.4)
RL.train()
values = RL.getValues()
actions = RL.getActions()
qvalues = RL.getQValues()
for i in values.keys():
print(f"{i} : {values[i]}, {actions[i]}")