print(loss[-1])
if (i % 20 == 0):
YY = np.argmax(a, axis=1)
error = BATCH_SIZE - sum(np.equal(batch_y, YY))
print(i, "epochs. Error:", error)
print("# 2s:", np.sum(np.equal(YY, 2)))
print("Training Done. Loss =", loss)
#a = Network.forward(X)
plt.figure()
plt.plot(loss)
plt.title('Loss over epochs')
window = gfx.GFX()
window.fps = 10e4 # you can modify this for debugging purposes, default=30
Game = png.PongModel(0.5, 0.5, 0.03, 0.01, 0.4) # initialize state
games = 0
totalscore = 0
while 1:
# main loop
if (window._open == False):
break
state = Game.get_state()
# state = np.divide(state-means, stds).tolist()
actionlist = Network.forward(state)
maxidx = 0
for i in range(1, len(actionlist)):
if (actionlist[i] > actionlist[maxidx]):
maxidx = i
Game.move_down()
def UpKey2(Game): # move player 2
Game.move_up(2)
def DownKey2(Game):
Game.move_down(2)
if __name__ == '__main__':
window = gfx.GFX(wall_x=0, player_x=1, players=2)
#window.fps = 5 # you can modify this for debugging purposes, default=30
Game = png.PongModel(0.5, 0.5, 0.03, 0.01, 0.4,
paddleX=1) # initialize state
Game.init2(0.4, 0)
#################################################### Ignore this
window.win.bind('<Up>', lambda eff: UpKey(Game))
window.win.bind('<Down>', lambda eff: DownKey(Game))
window.win.bind('w', lambda eff: UpKey2(Game))
window.win.bind('s', lambda eff: DownKey2(Game))
######################################################## /ignore
while 1:
# main loop
if (window._open == False):
break
Game.update(window)
if __name__ == '__main__':
# Load data
X, y = load_data('./data/expert_q.txt')
# Initialize the model
nn = Network(hidden_layers=(256, 256, 256, 256),
lr=9e-2,
epoch=600,
bias=True,
batch_size=64)
try:
nn.load_weights()
except:
print('WARNING: No pre-trained networks!')
# Training
nn.train(X, y)
nn.save_weights()
# Display accuracy
accuracy(nn, X, y)
print()
# Simulate 1,000 games
N = 1000
environment = pm.PongModel(0.5, 0.5, 0.03, 0.01, 0.4)
window = gfx.GFX()
window.fps = 4e16
avg_score = simulate_game(N, window, environment, nn)
print('Avg score of {0} games:{1:.3f}'.format(N, avg_score))
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 26 00:02:49 2018
@author: Shiratori
"""
import graphics as gfx
import pong_model as pm
from q_learning import QLearning
if __name__ == '__main__':
# Set up environment
environment = pm.PongModel(0.5, 0.5, -0.03, 0.01, 0.4, paddleX=0)
window = gfx.GFX(wall_x=1, player_x=0)
window.fps = 9e16
# Set up model
model = QLearning(environment, window, C=5e3, gamma=0.99, explore=-1,
threshold=-1, log=True, log_file='q_test_log_(agent_B).txt',
mode='test', q_table_file='q_q_table_(agent_B).csv')
# Training
model.train()
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 26 00:02:49 2018
@author: Shiratori
"""
import graphics as gfx
import pong_model as pm
from q_learning import QLearning
if __name__ == '__main__':
# Set up environment
environment = pm.PongModel(
0.5, 0.5, -0.03, 0.01, 0.5, paddleX=0
) #initial state changed; defending line on the left. also, paddle now on left, so paddleX changed
window = gfx.GFX(wall_x=1, player_x=0) #switched layout
window.fps = 9e16
# Set up model
model = QLearning(environment,
window,
C=5e3,
gamma=0.99,
explore=-1,
threshold=-1,
log=True,
log_file='q_test_log_(1_1)_A.txt',
mode='test',
q_table_file='q_q_table_(1_1)_A.csv')
