def get_next_move(self, m: mc.Mancala) -> int:
state = m.get_board_status()
# state[mc.PLAYER1_BANK] = 0
# state[mc.PLAYER2_BANK] = 0
test_m = mc.Mancala()
test_m.set_board_status(self.p, state)
best_score = -100
best_move = -1
moves = test_m.get_valid_moves()
for i in moves:
# check the minimax value of each valid move
# print("top level: move %i" % i)
test_m.set_board_status(self.p, state)
test_m.play_turn(i)
test_score = self.minimax(test_m, self._depth)
# best_score, best_move = max((best_score, best_move), (test_score, i), key=lambda x: x[0])
if test_score > best_score:
best_score = test_score
best_move = i
print("top level: move %i, score %i" % (i, test_score))
return best_move
import tensorflow as tf, mancala, numpy as np, random
from collections import deque
import matplotlib.pyplot as plt
game = mancala.Mancala()
retrain = False
model = tf.keras.Sequential([
tf.keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(14, )),
tf.keras.layers.Dense(16, activation=tf.nn.relu),
tf.keras.layers.Dense(16, activation=tf.nn.relu),
tf.keras.layers.Dense(14)
])
learning_rate = 1e-3
optimizer = tf.keras.optimizers.Adam(learning_rate)
model.compile(loss=tf.keras.losses.mean_squared_error.__name__,
optimizer=optimizer)
num_episodes = 2000
num_turns_per_episode = 500
epsilon = 1.0
epsilon_min = 0.5
epsilon_decay = 0.995
gamma = 0.95
if retrain:
fig = plt.figure()
import reinforcement_learning_model as avm import mancala as mancala import matplotlib.pyplot as plt if __name__ == "__main__": manc = mancala.Mancala() #create an instance of our rl agent without any discounting factor (we don't care if we win in 5 steps or 10) rl_agent = avm.ActionValueModel(epsilon = 0.5, discounting_factor = lambda a : 1) #map out valid actions for each player actions_bottom = [(1, "bottom"), (2, "bottom"), (3, "bottom"), (4, "bottom"), (5, "bottom"), (6, "bottom")] actions_top = [(7, "top"), (8, "top"), (9, "top"), (10, "top"),
# ---------------------------------------------------------------------------
# EVOLUTION LOOP:
for gen in range(start_gen, end_gen):
# Resets agent score so old agents don't get an advantage.
for agent in population:
agent.score = 0
# Matches up all the agents.
matchups = [(a, b) for a in population for b in population]
for players in matchups:
# Creates the mancala game object for each matchup.
game = mncl.Mancala()
# Agents take turns chosing stones.
while game.is_active:
# Takes the game board and changes into a column vector for the agents to process.
board_input = np.array(game.board).reshape((len(game.board), 1))
# Calculates output of the neural network for the given boardstate input.
output = players[game.player].choose(board_input)
# Reshapes the output into a list of prioritised choices.
choices = np.argsort(output.reshape((1, 6)))
# Test each choice in turn until it finds a valid move.
n = 0
while not game.valid_choice(choices[0][n]):
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 13 11:20:54 2020
@author: johanna
"""
import numpy as np
import random
import mancala as m
import matplotlib.pyplot as plt
ma = m.Mancala()
#ma.net.generate_random_network([6,12,6,2])
#input()
ma.name = 'testeinfach1'
print("Start")
#print(ma.net.biases)
print(ma.spielfeld)
ma.print_spielfeld()
#ma.train_net(1,10,0.1,5)
print("trained")
#print(ma.play())
print('play gegen Random')
ma.net.generate_random_network([14, 10, 6])
ma.name = 'testeinfach1'
print("Start")
ma.print_spielfeld()
Spieler1 = np.array([0])
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jul 15 14:15:22 2020
@author: johanna
"""
import numpy as np
import mancala as m
ma = m.Mancala(exploration_rate=0.2)
print("Start")
print(ma.net.biases)
print(ma.spielfeld[0:12])
#ma.train_net(500,25,1)
print("trained")
#print(ma.play())
print('play gegen Random')
matest = m.Mancala(exploration_rate=0.8)
matest.net.load_network_from_files("Test")
Spieler1gewonnen = 0
Spieler2gewonnen = 0
unentschieden = 0
for i in range(1, 2):
#print(i)
while not (
np.array_equal(matest.spielfeld[0:6], [0, 0, 0, 0, 0, 0])
or np.array_equal(matest.spielfeld[6:12], [0, 0, 0, 0, 0, 0])
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Jul 13 11:20:54 2020
@author: johanna
"""
import numpy as np
import random
import mancala as m
matest = m.Mancala(exploration_rate = 0.3, name = "Test", network_layers = 4)
#lasse Netz als Spieler 1 gegen Random spielen
Spieler1gewonnen = 0
Spieler2gewonnen = 0
unentschieden = 0
k = 50000
for i in range (1,k):
#print(i)
spielfelder = []
while not(np.array_equal(matest.spielfeld[0:6] ,[0,0,0,0,0,0]) or np.array_equal(matest.spielfeld[6:12] ,[0,0,0,0,0,0]) or matest.spielfeld[12]>36 or matest.spielfeld[13]>36):
#Spieler 1 netz
feld = matest.get_next_action(matest.spielfeld)
matest.spielfeld, reward = matest.get_spielfeld_and_reward_after_action(matest.spielfeld, feld)
spielfelder.append(matest.spielfeld)
import Network as net
import mancala as man
import numpy as np
import os
a = man.Mancala(exploration_rate=1.0)
a.name = "Net"
a.name2 = "Net2"
a.net.generate_random_network([14, 14, 14, 14, 14, 6])
a.net2.generate_random_network([14, 14, 14, 14, 14, 6])
rate = []
size = 1000
for i in range(size):
print("Training")
a.train_dq(100, 10, 0.2, 10)
a.exploration_rate -= 1 / (size + size / 10)
rate.append(a.get_win_rate(1000))
np.savetxt("rate.csv", rate, delimiter=',')
import mancala
import minimax
m = mancala.Mancala("Human", "CPU")
ai = minimax.MiniMax(mancala.PLAYER2, depth=5)
while not m.is_game_over():
try:
print(m)
if m.current_player() == mancala.PLAYER1:
move = int(
input("current player: %s" % m.get_current_player_name()))
else:
print("getting move from AI")
move = ai.get_next_move(m)
print("AI chose %i" % move)
m.play_turn(move)
except Exception as e:
print(e.message)
print(m.get_winner() + " wins!")
