def test(mainQN,sess):
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint('chk/dqvrand'))
e = 0
logs = []
wins = []
game = Game(verbose=False)
while e <=1000:
e+=1
if not game.game_over:
state = game.space
feed = {mainQN.inputs_: state.reshape((1, *state.shape))}
As = sess.run(mainQN.output, feed_dict=feed)
avail = game.avail()
availQ = {}
for k in avail:
availQ[k]=As[0][k]
action = max(availQ,key=availQ.get)
game.move(action,1)
game.step(1)
if game.game_over:
wins.append(game.game_over)
log = game.setup()
logs.append(log)
continue
move = game.random_space()
game.move(move,2)
game.step(2)
win, comp, bloc = 0, 0, 0
c = Counter(wins)
r = GameRate(verbose=False, list=logs,player=1,opponent=2)
r.check_games()
#print(r,c)
win= c[1] / len(wins)
print("win percentage",win)
if (r.completions + r.missed_completions)>0:
comp = r.completions / (r.completions + r.missed_completions)
print("immediate completions",comp)
if (r.blocks + r.missed_blocks)>0:
bloc = r.blocks / (r.blocks + r.missed_blocks)
print("blocks",bloc)
#exit(1)
if win ==0.0:
print(wins)
exit(1)
return win,comp,bloc
for ep in range(1, train_episodes):
total_reward = 0
t = 0
while t < max_steps:
if not game.game_over:
step += 1
explore_p = explore_stop + (explore_start - explore_stop) * np.exp(-decay_rate * step)
if explore_p > np.random.rand():
# Make a random action
action = game.random_space()
else:
# Get action from Q-network
feed = {mainQN.inputs_: state.reshape((1, *state.shape))}
Qs = sess.run(mainQN.output, feed_dict=feed)
#print(Qs)
avail = game.avail()
availQ = {}
for i in avail:
availQ[i]=Qs[0][i]
action = max(availQ,key=availQ.get)
game.move(action, 1)
next_state, reward = game.step()
total_reward += reward
if game.game_over:
# the episode ends so no next state
next_state = np.zeros(state.shape)
t = max_steps
if loss:
print('Episode: {}'.format(ep),
'Total reward: {}'.format(total_reward),
def test(sess, actor1, actor2):
game = Game(verbose=False)
logs = []
wins = []
for i in range(TEST_EPISODES):
game.setup()
s = game.space
terminal = False
for j in range(MAX_EP_STEPS):
if not terminal:
a = actor1.predict(np.reshape(game.space, (1, *s.shape)))
avail = game.avail()
availQ = {}
for i in avail:
availQ[i] = a[0][i]
action = max(availQ, key=availQ.get) #game.random_space()
#
# print(a)
game.move(action, 1)
s2, r = game.step(1)
terminal = game.game_over
info = None
if terminal:
wins.append(game.game_over)
log = game.setup()
logs.append(log)
s = game.space
break
else:
a = actor2.predict(np.reshape(game.space, (1, *s.shape)))
avail = game.avail()
availQ = {}
for i in avail:
availQ[i] = a[0][i]
action = max(availQ, key=availQ.get)
# print(a)
game.move(action, 2)
s2, r = game.step(1)
terminal = game.game_over
info = None
c = Counter(wins)
r = GameRate(verbose=False, list=logs, player=1, opponent=2)
r2 = GameRate(verbose=False, list=logs, player=2, opponent=1)
bloc1, bloc2 = 0, 0
r.check_games()
r2.check_games()
win_p1 = c[1] / (TEST_EPISODES - 1)
print("1win percentage", win_p1)
if r.completions + r.missed_completions > 0:
comp1 = r.completions / (r.completions + r.missed_completions)
else:
comp1 = 0
print("1immediate completions", comp1)
if r.blocks + r.missed_blocks > 0:
bloc1 = r.blocks / (r.blocks + r.missed_blocks)
win_p2 = c[2] / (TEST_EPISODES - 1)
print("2win percentage", win_p2)
if r2.completions + r2.missed_completions > 0:
comp2 = r2.completions / (r2.completions + r2.missed_completions)
else:
comp2 = 0
print("2immediate completions", comp2)
if r2.blocks + r2.missed_blocks > 0:
bloc2 = r2.blocks / (r2.blocks + r2.missed_blocks)
return win_p1, comp1, bloc1, win_p2, comp2, bloc2
