def playGame(me, thehost, sess):
sock = initClient(me, thehost)
othello = Othello(0, state, me)
while (True):
print "Read"
status = readMessage(sock)
othello.reset(status[1], state, me)
if (status[0] == me):
if status[1] < 30:
action_index = othello.smartMove()
else:
_state = get_state(me)
_moves = valid_moves(getValidMoves(status[1], me))
q = sess.run(out, feed_dict={
"train/inputs:0": [_state], "train/moves:0": [_moves]
})[0]
action = nonzero_max(q)
action_index = (action//8, action % 8)
sel = str(action_index[0]) + "\n" + str(action_index[1]) + "\n"
print "<" + sel + ">"
sock.send(sel)
print "sent the message"
else:
print "It isn't my turn"
return
sess = tf.InteractiveSession()
# Set hyper parameters and variables
M = 1000; '''Number of episodes'''
LEARNING_RATE = 0.001; '''Base learning rate'''
GAMMA = 0.99; '''Discount factor'''
BUFFER_SIZE = 100000
BATCH_SIZE = 64
RANDOM_SEED = 1234
dim = 8
# Set up environment
env = Othello(dim)
state = state_prime = env.reset()
action = np.zeros(len(state))
# create deep q network
agent = DeepQNetwork(sess, state, action, LEARNING_RATE, 0.001, GAMMA)
sess.run(tf.initialize_all_variables())
agent.update_target_network()
# Initialize replay buffer Replay
Replay = ReplayBuffer(BUFFER_SIZE, random_seed=RANDOM_SEED, prioritized=False)
def nonzero_max(actions):
indices = np.nonzero(actions)[0]
mapping = []
for index in indices:
