def simulate(self, node: Node, my_env: TicTacToeEnv) -> float:
"""
MCTS: Simulation stage.
- Randomly play out remainder of moved and report reward
Won reward=1, Tie reward=0.5, Lost reward=0
"""
state = node.state
while not my_env.done:
action = random.choice(my_env.available_actions())
state, _, _, _ = my_env.step(action)
return self.compute_reward(state)
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 select(self, node: Node, my_env: TicTacToeEnv):
"""
MCTS: Selection stage.
- If node has any unvisted children, select one
- If all children visited, choose best by UCB score
and advance environment
- If no children, return itself.
"""
while node.children:
# if any unexplored children, pick one
univisted_children = [c for c in node.children if c.unvisited]
if univisted_children:
node = univisted_children[0]
# otherwise, choose best child according to ucb
else:
node = max(node.children, key=self.ucb_score)
# step copied environment forward
my_env.step(node.action)
return node, my_env
def run():
env = TicTacToeEnv()
state = torch.tensor([one_hot_board(env.reset())], dtype=torch.float).to(model.device)
for step in range(n_step):
t = np.clip(step / eps_steps, 0, 1)
eps = (1 - t) * eps_start + t * eps_end
print('\r'+f'step----{step},epsilon----{eps}', flush=True, end='')
action, _ = model.select_action(state, eps)
next_state, reward, done, _ = env.step(action.item())
next_state=one_hot_board(next_state)
if not done:
next_state, _, done, _ = env.step(
model.select_dummy_action(next_state))
next_state=one_hot_board(next_state)
next_state = torch.tensor(
[next_state], dtype=torch.float).to(model.device)
if done:
next_state = None
model.Memory.memorize(state, action, next_state,
torch.tensor([reward], device=model.device))
state = next_state
model.learn()
if done:
state = torch.tensor(
[one_hot_board(env.reset())], dtype=torch.float).to(model.device)
if step % target_update == 0:
model.target_update()
def expand(self, node: Node, my_env: TicTacToeEnv):
"""
MCTS: Expansion stage.
- If additional moves are possible from given node
child nodes will be created, one selected, and env advanced.
- If not, same node and env will be returned.
"""
# If this is a terminal state, don't try to expand
if my_env.done:
return node, my_env
# Add a child node for each possible action
for action in my_env.available_actions():
nstate = after_action_state(node.state, action)
Node(nstate, action, parent=node)
# If node has children after expansion, select one
if node.children:
node = random.choice(node.children)
my_env.step(node.action)
return node, my_env
def act(self, state, my_env: TicTacToeEnv):
available_actions = my_env.available_actions()
# --- Step 1: play winning move, if possible ---
for action in available_actions:
nstate = after_action_state(state, action)
gstatus = check_game_status(nstate[0])
if gstatus > 0:
if tomark(gstatus) == self.mark:
return action
# --- Step 2: block opponent from winning ---
# imagine the opponent was playing
rev_state = (state[0], next_mark(state[1]))
for action in available_actions:
nstate = after_action_state(rev_state, action)
gstatus = check_game_status(nstate[0])
if gstatus > 0:
# if they can make a winning move, play that
if tomark(gstatus) == self.opponent_mark:
return action
return random.choice(available_actions)
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)
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(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)
# 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 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
from utils import one_hot_board, Net
from algorithm import DQN
import torch
def run(player):
done = False
obs = env.reset()
env.render()
while not done:
if obs[1] == player:
action = int(input())
obs_, _, done, info = env.step(action)
else:
obs = one_hot_board(obs)
action = model.act(torch.tensor([obs], dtype=torch.float)).item()
obs_, _, done, exp = env.step(action)
obs = obs_
env.render()
if __name__ == "__main__":
env = TicTacToeEnv()
model = DQN()
model.load_net('net.pkl')
print('choose O or X player')
player = str(input(''))
assert player == 'O' or player == 'X'
run(player)
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):
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 _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 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(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 _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 _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()
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 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')