def play(max_episode=10):
episode = 0
start_mark = 'O'
env = TicTacToeEnv()
agents = [BaseAgent('O'), BaseAgent('X')]
while episode < max_episode:
env.set_start_mark(start_mark)
state = env.reset()
_, mark = state
done = False
while not done:
env.show_turn(True, mark)
agent = agent_by_mark(agents, mark)
ava_actions = env.available_actions()
action = agent.act(state, ava_actions)
state, reward, done, info = env.step(action)
env.render()
if done:
env.show_result(True, mark, reward)
break
else:
_, mark = state
# rotate start
start_mark = next_mark(start_mark)
episode += 1
def play(show_number):
env = TicTacToeEnv(show_number=show_number)
agents = [MinimaxAgent('O'),
HumanAgent('X')]
episode = 0
while True:
state = env.reset()
_, mark = state
done = False
env.render()
while not done:
agent = agent_by_mark(agents, mark)
env.show_turn(True, mark)
ava_actions = env.available_actions()
if mark=='O':
n,action=agent.act(state, ava_actions)
else:
action = agent.act(state, ava_actions)
if action is None:
sys.exit()
state, reward, done, info = env.step(action)
print('')
env.render()
if done:
env.show_result(True, mark, reward)
break
else:
_, _ = state
mark = next_mark(mark)
episode += 1
def _bench(max_episode, model_file, show_result=True):
"""Benchmark given model.
Args:
max_episode (int): Episode count to benchmark.
model_file (str): Learned model file name to benchmark.
show_result (bool): Output result to stdout.
Returns:
(dict): Benchmark result.
"""
minfo = load_model(model_file)
agents = [BaseAgent('O'), TDAgent('X', 0, 0)]
show = False
start_mark = 'O'
env = TicTacToeEnv()
env.set_start_mark(start_mark)
episode = 0
results = []
for i in tqdm(range(max_episode)):
env.set_start_mark(start_mark)
state = env.reset()
_, mark = state
done = False
while not done:
agent = agent_by_mark(agents, mark)
ava_actions = env.available_actions()
action = agent.act(state, ava_actions)
state, reward, done, info = env.step(action)
if show:
env.show_turn(True, mark)
env.render(mode='human')
if done:
if show:
env.show_result(True, mark, reward)
results.append(reward)
break
else:
_, mark = state
# rotation start
start_mark = next_mark(start_mark)
episode += 1
o_win = results.count(1)
x_win = results.count(-1)
draw = len(results) - o_win - x_win
mfile = model_file.replace(CWD + os.sep, '')
minfo.update(
dict(base_win=o_win, td_win=x_win, draw=draw, model_file=mfile))
result = json.dumps(minfo)
if show_result:
print(result)
return result
def _play(load_file, vs_agent, show_number):
"""Play with learned model.
Make TD agent and adversarial agnet to play with.
Play and switch starting mark when the game finished.
TD agent behave no exploring action while in play mode.
Args:
load_file (str):
vs_agent (object): Enemy agent of TD agent.
show_number (bool): Whether show grid number for visual hint.
"""
load_model(load_file)
env = TicTacToeEnv(show_number=show_number)
td_agent = TDAgent('X', 0, 0) # prevent exploring
start_mark = 'O'
agents = [vs_agent, td_agent]
while True:
# start agent rotation
env.set_start_mark(start_mark)
state = env.reset()
_, mark = state
done = False
# show start board for human agent
if mark == 'O':
env.render(mode='human')
while not done:
agent = agent_by_mark(agents, mark)
human = isinstance(agent, HumanAgent)
env.show_turn(True, mark)
ava_actions = env.available_actions()
if human:
action = agent.act(ava_actions)
if action is None:
sys.exit()
else:
action = agent.act(state, ava_actions)
state, reward, done, info = env.step(action)
env.render(mode='human')
if done:
env.show_result(True, mark, reward)
break
else:
_, mark = state
# rotation start
start_mark = next_mark(start_mark)
def play(show_number):
env = TicTacToeEnv(show_number=show_number)
agents = [HumanAgent(HUMAN_MARK)]
episode = 0
j = 0
while True:
state = env.reset()
_, mark = state
done = False
env.render()
i = 0
if j == 0:
Papa = Node(state, None, [1, 2, 3, 4, 5, 6, 7, 8, 9], 0)
Papa.fill()
j += 1
action = Papa.maxAddress
current = Papa.children[Papa.maxAddress]
print("X's Turn")
while not done:
pre_action = action
pre_current = current
ava_actions = env.available_actions()
if i % 2 == 0 and i != 0:
print("X's Turn")
print("Previous Action: ", pre_action)
action, current = pre_current.reach_child(pre_action)
print("Playing: ", action)
elif i % 2 == 1:
print("O's Turn")
action = HumanAgent.act(state, ava_actions)
i += 1
if action is None:
sys.exit()
state, reward, done, info = env.step(action - 1)
print('')
env.render()
if done:
env.show_result(True, mark, reward)
break
else:
_, _ = state
mark = next_mark(mark)
episode += 1