def play_game_with_visualization(network_name='cluster_net', points_name='large_visualization'):
game = toy_mr.ToyMR('../mr_maps/four_rooms.txt')
network.saver.restore(network.sess, './'+network_name+'.ckpt')
scatter_points = np.load('./'+points_name+'.npy')
f, ax = plt.subplots()
while True:
if game.is_current_state_terminal():
game.reset_environment()
for event in pygame.event.get():
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_UP:
action = toy_mr.NORTH
elif event.key == pygame.K_DOWN:
action = toy_mr.SOUTH
elif event.key == pygame.K_RIGHT:
action = toy_mr.EAST
elif event.key == pygame.K_LEFT:
action = toy_mr.WEST
else:
action = -1
if action != -1:
game.perform_action(action)
state = game.get_current_state()[0]
plt.pause(1)
ax.cla()
ax.scatter(scatter_points[:, 0], scatter_points[:, 1], color='blue')
[e1] = network.sess.run([network.e1], feed_dict={network.inp_state1: [state]})
ax.scatter(e1[:, 0], e1[:, 1], color='red')
plt.draw()
def setup_toy_mr_env():
env = toy_mr.ToyMR('../mr_maps/full_mr_map.txt',
max_lives=5,
repeat_action_probability=0.25,
use_gui=use_gui)
num_actions = len(env.get_actions_for_state(None))
return env, num_actions
def setup_four_rooms_env():
env = toy_mr.ToyMR('../mr_maps/four_rooms.txt', max_num_actions=10000)
num_actions = len(env.get_actions_for_state(None))
return env, num_actions
def setup_toy_mr_env():
env = toy_mr.ToyMR('../mr_maps/full_mr_map.txt')
num_actions = len(env.get_actions_for_state(None))
return env, num_actions
import numpy as np
import pygame
import tqdm
import toy_mr
import toy_mr_encoder
import pc_cts
map_file = '../mr_maps/full_mr_map.txt'
game = toy_mr.ToyMR(map_file)
print('Collecting data...')
states = []
for i in range(0, 10000):
if game.is_current_state_terminal():
game.reset_environment()
states.append(toy_mr_encoder.encode_toy_mr_state(game))
action = np.random.choice([0, 1, 2, 3])
game.perform_action(action)
print('Training...')
cts = pc_cts.LocationDependentDensityModel((11, 11), lambda x, y: x,
pc_cts.L_shaped_context)
for i in tqdm.trange(1000):
ii = np.random.randint(0, len(states))
cts.update(states[ii])
print('Ready!')
game.reset_environment()
running = True
while running:
def setup_toy_mr_env():
env = toy_mr.ToyMR('./mr_maps/full_mr_map.txt', abstraction_file='./mr_maps/full_mr_map_abs.txt', use_gui=True)
num_actions = len(env.get_actions_for_state(None))
return env, num_actions
