def SARSA(q, s, a, newState, epsilon, alpha, gamma):
reward = tictactoe.observeReward(q.player, newState)
if newState.terminal():
newQ = reward
else:
newAction = tictactoe.chooseAction(q, newState, epsilon)
newQ = q.Q(newState, newAction)
newScore = q.Q(s, a) + alpha*(reward + gamma*newQ - q.Q(s, a))
q.update(s, a, newScore)
def rewardPerEpisode(q, gamma):
if q.player == tictactoe.PlayerCircle:
opponent = tictactoe.ActionValueFunc(tictactoe.PlayerCross)
else:
opponent = tictactoe.ActionValueFunc(tictactoe.PlayerCircle)
rpe = 0.0 # reward per episode
t = 0 # time step
s = tictactoe.State()
# Randomly determine whether the player or her opponent should move first.
if random.random() < 0.5:
a = tictactoe.chooseAction(opponent, s, 0)
s = tictactoe.takeAction(opponent.player, s, a)
t += 1
while True:
# Player makes a move and defers observing the reward until her opponent has made his move.
# Only under the special case where the move is the last move should the player observe reward before exiting.
a = tictactoe.chooseAction(q, s, 0)
s1 = tictactoe.takeAction(q.player, s, a)
t += 1
if s1.terminal():
reward = tictactoe.observeReward(q.player, s1)
rpe += math.pow(gamma, t) * reward
break
# Opponent make a move, and the resulting state is observed by player to calculate her reward.
opponentAction = tictactoe.chooseAction(opponent, s1, 0)
s2 = tictactoe.takeAction(opponent.player, s1, opponentAction)
t += 1
reward = tictactoe.observeReward(q.player, s2)
rpe += math.pow(gamma, t) * reward
s = s2
if s.terminal():
break
return rpe
def QLearning(q, s, a, newState, alpha, gamma): reward = tictactoe.observeReward(q.player, newState) newScore = q.Q(s, a) + alpha*(reward + gamma*q.best(newState) - q.Q(s, a)) q.update(s, a, newScore)
