def ql_vs_minmax(visualise):
print("ql vs minmax ql")
numActions = env.n_actions
drawProbability = 0.1
decay = 10**(-2. / N_EPISODES * 0.05)
if(visualise):
vis = Visualiser(env, 80)
numActions = env.n_actions
start_time = time.time()
ql_wins = 0
minmax_wins = 0
playerA = QLearn(actions=list(range(numActions)), reward_decay=0.7)
playerB = MinimaxQPlayer(numActions, numActions, decay=decay, expl=0.01, gamma=1-drawProbability)
playerA.load_Qtable('saved_players/QR')
playerB.load_Qtable("MR")
# no explore
iterations = 5000
for episode in range(iterations):
# initial observation
observation = env.reset()
# print(str(episode))
if(episode % 100 == 0):
print(str(float(episode) / iterations * 100) + "%")
# if(episode > iterations - 100):
# vis.update_canvas(env)
while True:
# RL choose action based on observation
actionA = playerA.choose_action(str(observation))
actionB = playerB.choose_action(str(observation))
# RL take action and get next observation and reward
observation_, reward, done = env.step(actionA, actionB)
if reward == 1:
ql_wins += 1
elif reward == -1:
minmax_wins += 1
observation = observation_
if(visualise):
vis.update_canvas(env)
if done:
if(visualise):
vis.reset()
break
return (ql_wins, minmax_wins)
def run_optimal():
vis = Visualiser(env, 80)
numActions = env.n_actions
playerA = QLearn(actions=list(range(numActions)))
playerA.load_Qtable("saved_players/QR")
playerB = QLearn(actions=list(range(numActions)))
playerB.load_Qtable("saved_players/QR_base")
for episode in range(500):
observation = env.reset()
vis.update_canvas(env)
while(True):
actionA = playerA.choose_action(str(observation))
actionB = playerB.choose_action(str(observation))
observation_, reward, done = env.step(actionA, actionB)
observation = observation_
vis.update_canvas(env)
if done:
vis.reset()
break
print("Games won: " + str(env.win_count))
vis.destroy()
def run_optimalB():
numActions = env.n_actions
drawProbability = 0.1
decay = 10**(-2. / N_EPISODES * 0.05)
vis = Visualiser(env, 80)
numActions = env.n_actions
playerB = MinimaxQPlayer(numActions, numActions, decay=decay, expl=0.00, gamma=1-drawProbability)
playerB.load_Qtable("MR")
playerA = QLearn(actions=list(range(numActions)))
playerA.load_Qtable("saved_players/MR_base")
for episode in range(20):
observation = env.reset()
vis.update_canvas(env)
while(True):
actionA = playerA.choose_action(str(observation))
actionB = playerB.choose_action(str(observation))
observation_, reward, done = env.step(actionA, actionB)
observation = observation_
vis.update_canvas(env)
if done:
vis.reset()
break
print("Games won: " + str(env.win_count))
vis.destroy()
