def play_base(env):
load_model(MC_MODEL_FILE)
agents = [BaseAgent('O'),
OffPolicyMCAgent('X', 0, 1)]
start_mark = 'O'
test_cases = 1000
win1, win2 = 0,0
while test_cases:
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()
env.show_turn(False,mark)
action = agent.act(state,ava_actions)
next_state, reward, done, _ = env.step(action)
state = next_state
if done:
#env.show_result(True, mark, reward)
if reward != 0 and mark == agents[0].mark:
win1 += 1
elif reward != 0 and mark == agents[1].mark:
win2 += 1
break
else:
_, mark = state
# rotation s tart
#start_mark = next_mark(start_mark)
test_cases-=1
print(agents[0].mark, win1, agents[1].mark, win2)
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_base(env):
load_model(MC_MODEL_FILE)
agents = [BaseAgent('O'), OnPolicyMCAgent('X', 0, 1)]
start_mark = 'X'
test_cases = 10
while test_cases:
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()
env.show_turn(False, mark)
action = agent.act(state, ava_actions)
next_state, reward, done, _ = env.step(action)
state = next_state
if done:
env.show_result(True, mark, reward)
break
else:
_, mark = state
# rotation start
start_mark = next_mark(start_mark)
test_cases -= 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()
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)
print((state,reward,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 _learnhuman(epsilon, alpha, save_file, load_file, vs_agent, show_number):
load_model(load_file)
env = TicTacToeEnv(show_number=show_number)
start_mark = 'X'
agents = [vs_agent, TDAgent('O', epsilon, alpha)]
max_episode = 0
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 == 'X':
env.render(mode='human')
while not done:
agent = agent_by_mark(agents, mark)
human = isinstance(agent, HumanAgent)
print("==================================")
env.show_turn(True, mark)
ava_actions = env.available_actions()
# print(ava_actions)
if human:
action = agent.act(ava_actions)
print("action is %s" % (action))
if action is None:
sys.exit()
else:
action = agent.act(state, ava_actions)
print("action is %s" % (action))
###
nstate, reward, done, info = env.step(action)
agent.backup(state, nstate, reward)
env.render(mode='human')
if done:
print("Return reward : " + str(reward))
env.show_result(True, mark, reward)
time.sleep(1)
# if reward == 1:
# _conlearn(700, epsilon, alpha, save_file, load_file)
# set terminal state value
set_state_value(state, reward)
break
else:
_, mark = state = nstate
# rotation start
# start_mark = next_mark(start_mark)
max_episode += 1
# print(max_episode)
save_model(save_file, max_episode, epsilon, alpha)
def _learn(max_episode, epsilon, alpha, save_file):
"""Learn by episodes.
Make two TD agent, and repeat self play for given episode count.
Update state values as reward coming from the environment.
Args:
max_episode (int): Episode count.
epsilon (float): Probability of exploration.
alpha (float): Step size.
save_file: File name to save result.
"""
reset_state_values()
env = TicTacToeEnv()
agents = [TDAgent('O', epsilon, alpha),
TDAgent('X', epsilon, alpha)]
start_mark = 'O'
for i in tqdm(range(max_episode)):
episode = i + 1
env.show_episode(False, episode)
# reset agent for new episode
for agent in agents:
agent.episode_rate = episode / float(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()
env.show_turn(False, mark)
action = agent.act(state, ava_actions)
# update (no rendering)
nstate, reward, done, info = env.step(action)
print((state,reward,action))
agent.backup(state, nstate, reward)
if done:
env.show_result(False, mark, reward)
# set terminal state value
set_state_value(state, reward)
_, mark = state = nstate
# rotate start
start_mark = next_mark(start_mark)
# save states
save_model(save_file, max_episode, epsilon, alpha)
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)
print((state,reward,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_against(agent_mc,agent_2,max_episode = 10,bench = True):
start_mark = 'O'
env = TicTacToeEnv()
env.set_start_mark(start_mark)
agents = [agent_mc,agent_2]
episode = 0
results = []
for i in 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()
# print(agent.mark)
# if agent.mark == 'O':
# print(agent.Q[state])
action = agent.act(state,ava_actions)
state,reward,done,_ = env.step(action)
# env.render()
if done:
results.append(reward)
break
else:
_,mark = state
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
if bench == False:
print("O_WINS = {},X_WINS = {},DRAW = {}".format(o_win,x_win,draw))
return float(o_win-x_win)/(max_episode)
def play(show_number):
env = TicTacToeEnv(show_number=show_number)
# print("101", env.available_actions())
ROLLOUTS = 1000
REWARD_FACTOR = 1 # because rollout agent is player 2
agents = [
HumanAgent('X'),
# HumanAgent('X')]
RolloutAgent('O', ROLLOUTS, REWARD_FACTOR)
]
episode = 0
while True:
state = env.reset()
_, mark = state
done = False
_ = os.system('clear')
print('')
env.render()
while not done:
agent = agent_by_mark(agents, mark)
env.show_turn(True, mark)
ava_actions = env.available_actions()
if agent.agenttype == 'rollout':
action = agent.act(ava_actions, env)
else:
action = agent.act(ava_actions)
if action is None:
sys.exit()
state, reward, done, info = env.step(action)
_ = os.system('clear')
print('')
env.render()
if done:
env.show_result(True, mark, reward)
# print("Reward:", reward)
break
else:
_, mark = state
time.sleep(10)
episode += 1
def learn(env):
max_episode = MAX_EPISODE
epsilon = EPSILON
agents = [OnPolicyMCAgent('X', epsilon), OnPolicyMCAgent('O', epsilon)]
agents[0].orig_actions = env.available_actions()
agents[1].orig_actions = env.available_actions()
start_mark = 'O'
#iterating through episodes
for episode in tqdm(range(max_episode)):
#env.show_episode(False,episode+1)
agents[0].trans_list = []
agents[1].trans_list = []
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()
env.show_turn(False, mark)
action = agent.act(state, ava_actions)
next_state, reward, done, _ = env.step(action)
#agent.update(state, new_state)
agent.trans_list.append((state, action, reward))
state = next_state
_, mark = state
agents[0].update()
agents[1].update()
if done:
env.show_result(False, mark, reward)
start_mark = next_mark(start_mark)
save_model(MC_MODEL_FILE)
def play(max_episode=10):
start_mark = 'O'
env = TicTacToeEnv()
agents = [BaseAgent('O'),
BaseAgent('X')]
for _ in range(max_episode):
env.set_start_mark(start_mark)
state = env.reset()
while not env.done:
_, mark = state
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()
env.show_result(True, mark, reward)
# rotate start
start_mark = next_mark(start_mark)
def play_game(qagent):
env = TicTacToeEnv()
opponent = RandomAgent('X')
start_mark = 'O'
env.set_start_mark(start_mark)
state = env.reset()
s, mark = state
done = False
agents = [qagent, opponent]
while not done:
env.render()
agent = agent_by_mark(agents, mark)
ava_actions = env.available_actions()
env.show_turn(False, mark)
action = agent.act(state, ava_actions)
nstate, reward, done, info = env.step(action)
print(f'state: {s}, action: {action}')
if done:
env.render()
env.show_result(True, mark, reward)
s, mark = state = nstate
def train_agents(opponent,
max_episode,
epsilon,
epsilon_decay,
alpha,
alpha_decay,
gamma,
render=False):
reset_state_values()
env = TicTacToeEnv()
if opponent == 'random':
agents = [
QAgent(env.observation_space.n, env.action_space.n, 'O', epsilon,
epsilon_decay, alpha, alpha_decay, gamma),
RandomAgent('X')
]
else: # Two Q agents
agents = [
QAgent(env.observation_space.n, env.action_space.n, 'O', epsilon,
epsilon_decay, alpha, alpha_decay, gamma),
QAgent(env.observation_space.n, env.action_space.n, 'X', epsilon,
epsilon_decay, alpha, alpha_decay, gamma)
]
start_mark = 'O'
agent_rewards = {'O': [], 'X': []}
episode = 0
for i in tqdm(range(max_episode)):
episode += 1
env.show_episode(False, episode)
# reset agent for new episode
for agent in agents:
agent.episode_rate = episode / float(max_episode)
env.set_start_mark(start_mark)
state = env.reset()
s, mark = state
done = False
while not done:
if render:
env.render()
agent = agent_by_mark(agents, mark)
ava_actions = env.available_actions()
env.show_turn(False, mark)
action = agent.act(state, ava_actions)
# update (no rendering)
nstate, reward, done, info = env.step(action)
agent.update(s, nstate[0], action, reward, done)
if done:
if render:
env.render()
env.show_result(render, mark, reward)
# set terminal state value
set_state_value(state, reward)
agent_rewards['O'].append(reward)
agent_rewards['X'].append(-reward)
s, mark = state = nstate
# rotate start
start_mark = next_mark(start_mark)
return agent_rewards, agent_by_mark(agents, 'O')
def _learnhuman1(epsilon, alpha, save_file, load_file, vs_agent, show_number):
load_model(load_file)
env = TicTacToeEnv(show_number=show_number)
start_mark = 'X'
agents = [vs_agent, TDAgent('O', epsilon, alpha)]
max_episode = 0
agent_temp = 6 #Set Start position at 6 to td_agent
human_temp = 0
while True:
# start agent rotation
env.set_start_mark(start_mark)
state = env.reset()
_, mark = state
done = False
turns = 0
human_diff = False
# show start board for human agent
if mark == 'X':
env.render(mode='human')
while not done:
agent = agent_by_mark(agents, mark)
human = isinstance(agent, HumanAgent)
print(
"======================================Switch Turn======================================"
)
env.show_turn(True, mark)
ava_actions = env.available_actions()
# print(ava_actions)
if human:
action = agent.act(ava_actions)
turns += 1
if turns == 1 and human_temp != action:
human_temp = action
human_diff = True
# print("Human action is %s"%(action))
# print("Turns == %s" % (turns))
# print("human_temp == %s" %(human_temp))
# print("human_diff == %s" %(human_diff))
if action is None:
sys.exit()
else:
action = agent.act(state, ava_actions)
if turns == 1 and human_diff == False:
action = agent_temp
print("------------> Human start in the same position")
print("------------> [Fix]Agent action is %s" %
(action + 1))
elif turns == 1 and human_diff == True:
agent_temp = action
print(
"------------> Human start in the diiferent position")
print("------------> [New]Agent action is %s" %
(action + 1))
else:
print("------------> Agent action is %s" % (action + 1))
###
nstate, reward, done, info = env.step(action)
agent.backup(state, nstate, reward)
env.render(mode='human')
if done:
print("Return reward : " + str(reward))
env.show_result(True, mark, reward)
time.sleep(1)
# if reward == 1:
# _conlearn(700, epsilon, alpha, save_file, load_file)
# set terminal state value
set_state_value(state, reward)
break
else:
_, mark = state = nstate
# rotation start
# start_mark = next_mark(start_mark)
max_episode += 1
# print(max_episode)
save_model(save_file, max_episode, epsilon, alpha)
def play(num_games, verbose=True):
"""
Test out two agents playing against each other.
Displays progress and result.
Parameters:
-----------
num_games: int
How many games to simulate
verbose: bool
If true, display play information during each game
If false, just display progress bar as simulations progress.
"""
# Print header
print("-" * 30)
print(f"Playing {num_games} games")
print(" * Player X: {}".format(players["X"].name))
print(" * Player O: {}".format(players["O"].name))
print("-" * 30)
# select random starting player
start_mark = random.choice(["X", "O"])
# keep track of who won
winners = []
# if verbose is false, display progress bar
if not verbose:
myrange = trange
else:
myrange = range
for _ in myrange(num_games):
# set up board
env = TicTacToeEnv()
env.set_start_mark(start_mark)
state = env.reset()
# init the agents
agents = [players["X"]("X"), players["O"]("O")]
# play until game is done
while not env.done:
_, mark = state
if verbose:
env.show_turn(True, mark)
agent = agent_by_mark(agents, mark)
action = agent.act(state, copy(env))
state, reward, _, _ = env.step(action)
if verbose:
env.render()
# append winner to list (-1=X, 1=0, 0=tie)
winners.append(reward)
# print out result
if verbose:
env.show_result(True, mark, reward)
# rotate start
start_mark = next_mark(start_mark)
# tally and display final stats
c = Counter(winners)
total = c[-1] + c[1] + c[0]
print("\nX won {} ({:.2%})".format(c[-1], c[-1] / total))
print("O won {} ({:.2%})".format(c[1], c[1] / total))
print("Tied {} ({:.2%})".format(c[0], c[0] / total))
def _learnhuman1(epsilon, alpha, save_file, load_file, vs_agent, show_number):
connection = pymysql.connect(host="localhost",
user="******",
passwd="",
database="tictactoe")
cursor = connection.cursor()
load_model(load_file)
env = TicTacToeEnv(show_number=show_number)
start_mark = 'X'
agents = [vs_agent, TDAgent('O', epsilon, alpha)]
max_episode = 0
agent_temp = 6 #Set Start position at 6 to td_agent
human_temp = 0
while True:
# start agent rotation
env.set_start_mark(start_mark)
state = env.reset()
_, mark = state
done = False
turns = 0
human_diff = False
# variable for save to database
db_nstate = []
db_nvalue = []
db_choose = 0
db_pick = 0
db_current_state = ''
db_action = 0
db_note = ''
# show start board for human agent
if mark == 'X':
env.render(mode='human')
while not done:
agent = agent_by_mark(agents, mark)
human = isinstance(agent, HumanAgent)
print(
"======================================Switch Turn======================================"
)
env.show_turn(True, mark)
ava_actions = env.available_actions()
# print(ava_actions)
if human:
action = agent.act(ava_actions)
turns += 1
if turns == 1 and human_temp != action:
human_temp = action
human_diff = True
# print("Human action is %s"%(action))
# print("Turns == %s" % (turns))
# print("human_temp == %s" %(human_temp))
# print("human_diff == %s" %(human_diff))
if action is None:
sys.exit()
nstate, reward, done, info = env.step(action)
else:
action = agent.act(state, ava_actions)
db_nstate, db_nvalue, db_choose, db_pick = agent.get_db()
if turns == 1 and human_diff == False:
action = agent_temp
db_note = 'repeat'
print("------------> Human start in the same position")
print("------------> [Fix]Agent action is %s" %
(action + 1))
elif turns == 1 and human_diff == True:
agent_temp = action
db_note = 'different'
print(
"------------> Human start in the diiferent position")
print("------------> [New]Agent action is %s" %
(action + 1))
else:
print("------------> Agent action is %s" % (action + 1))
nstate, reward, done, info = env.step(action)
db_table_tictactoe_state = "INSERT INTO Tictactoe_state(EPISODE, NSTATE, NVALUE, CHOOSE, PICK, STATE_NOW, ACTION, NOTE) \
VALUES(%d, '%s', '%s', %d, %d, '%s', %d, '%s');" % (
max_episode, db_nstate, db_nvalue, db_choose, db_pick,
remove_X(nstate), action, db_note)
cursor.execute(db_table_tictactoe_state)
connection.commit()
###
agent.backup(state, nstate, reward)
env.render(mode='human')
if done:
db_win_state = ''
if reward == 0:
db_win_state = "draw"
else:
db_win_state = mark
print("Return reward : " + str(reward))
db_table_check_win = "INSERT INTO Check_win(REWARD, WIN_STATE) VALUES(%d, '%s');" % (
reward, db_win_state)
cursor.execute(db_table_check_win)
connection.commit()
env.show_result(True, mark, reward)
time.sleep(1)
# if reward == 1:
# _conlearn(700, epsilon, alpha, save_file, load_file)
# set terminal state value
set_state_value(state, reward)
break
else:
_, mark = state = nstate
connection.commit()
# insert2 = "INSERT INTO Tictactoe_state(EPISODE, NSTATE, REWARD, ALL_CHOICE, CHOOSE, PICK, STATE_NOW, \
# ACTION, NOTE) VALUES(1, 'asdasd', 1, 'asdasd', 1, 1, 'asdasd', 1, 'asdasasd');"
# cursor.execute(insert2)
# connection.commit()
# rotation start
# start_mark = next_mark(start_mark)
max_episode += 1
# print(max_episode)
save_model(save_file, max_episode, epsilon, alpha)
connection.close()
