restore = False
input_size = 9
output_size = 9
max_batch_size = 1000000
learning_rate = 0.001
discount_factor = 0.9
epsilon = 0.1
train_episode = 1000
verify_episode = 10000
env = tictactoe()
agent = AIagent_RL(learning_rate=learning_rate, restore=restore)
agent_base = AIagent_Base()
def update(agent, batch, dis=discount_factor):
x_stack = np.empty(0).reshape(0, input_size)
y_stack = np.empty(0).reshape(0, output_size)
for state, action, reward, next_state, next_turn, done in batch:
action_value = agent.action_value.predict(state)
action_value = np.reshape(action_value, 9)
if not done:
next_action = agent.policy(next_state, next_turn, epsilon=0)
next_action_value = agent.action_value.predict(next_state)
import copy
from Tictactoe_Env import tictactoe
from Agent import AIagent_RL, AIagent_Base, Human_agent
env = tictactoe()
agent1 = AIagent_RL(restore=True)
agent2 = Human_agent()
def play():
done = 0
winner = 0
env.reset()
state = copy.copy(env.state)
i = 0
while not done:
i += 1
turn = copy.copy(env.turn)
if i % 2 == 1:
action = agent1.policy(state, turn, epsilon=0)
else:
action = agent2.policy(state, turn, epsilon=0)
next_state, done, reward, winner = env.step(action)
state = copy.copy(next_state)
env.render()
if winner == 0:
print("Draw!")
else:
print("Winner is agent %d!" % winner)
import matplotlib.pyplot as plt
import copy
import itertools
from Tictactoe_Env import tictactoe, predict, ret_turn
from Agent import AIagent_RL, AIagent_Base
from Functions import is_finished, available_actions
verify_episode = 100
discount_factor = 0.9
env = tictactoe()
agent = AIagent_RL(restore=False)
agent_base = AIagent_Base()
iteration_plt = []
v_plt = []
wr_plt = []
def policy_evaluation(agent):
theta = 1e-9
while True:
delta = 0.0
state_list = itertools.product([0, 1, 2], repeat=9)
for state in state_list:
state = list(state)
done, winner = is_finished(state)
if not done: # except for terminal state
v = agent.value(state)
import copy
from Tictactoe_Env import tictactoe
from Agent import AIagent_RL, AIagent_Base, Human_agent
env = tictactoe()
agent1 = Human_agent()
agent2 = AIagent_RL(restore=True)
def play():
done = 0
winner = 0
env.reset()
state = copy.copy(env.state)
i = 0
while not done:
i += 1
turn = copy.copy(env.turn)
if i % 2 == 1:
action = agent1.policy(state, turn, epsilon=0)
else:
action = agent2.policy(state, turn, epsilon=0)
next_state, done, reward, winner = env.step(action)
state = copy.copy(next_state)
env.render()
if winner == 0:
print("Draw!")
else:
print("Winner is agent %d!" % winner)