def main():
# setup vars
rom = b'D:\\_code\\_reinforcementlearning\\breakout.bin'
gamename = 'breakout'
skip_frame = 4
num_actions = 4
learner = PrioritizedExperienceLearner(skip_frame, num_actions)
game_handler = GameHandler(rom, False, learner, skip_frame)
scoreList = list()
bestTotReward = -np.inf
# plt.ion()
st = time.time()
for episode in range(50):
total_reward = game_handler.run_one_game(learner, lives=5, life_ram_ind=BREAKOUT, early_return=True)
scoreList.append(total_reward)
learner.game_over()
# if this is the best score save it as such
if total_reward >= bestTotReward:
learner.save('dqnbest{0}.pkl'.format(total_reward))
bestTotReward = total_reward
# plot cost and score
plt.clf()
plt.subplot(1, 2, 1)
plt.plot(learner.get_cost_list(), '.')
plt.subplot(1, 2, 2)
sl = np.asarray(scoreList)
plt.plot(sl, '.')
# plt.pause(0.01)
# save params every 10 games
if episode % 10 == 0:
learner.save('dqn{0}.pkl'.format(episode))
et = time.time()
print("Episode " + str(episode) + " ended with score: " + str(total_reward))
print('Total Time:', et - st, 'Frame Count:', game_handler.total_frame_count, 'FPS:', game_handler.total_frame_count / (et - st))
# plt.ioff()
plt.show()
# final save
learner.save('dqn{0}.pkl'.format(episode+1))
def main():
game_handler = GameHandler(rom, False, learner, skip_frame)
scoreList = list()
validLossList = list()
bestTotReward = -np.inf
# plt.ion()
st = time.time()
for episode in range(2):
learner.copy_new_target() # copy a new target learner at the start of each game
total_reward = game_handler.run_one_game(learner, lives=5, life_ram_ind=BREAKOUT, early_return=True)
scoreList.append(total_reward)
learner.game_over()
# if this is the best score save it as such
if total_reward >= bestTotReward:
learner.save('dqnbest{0}.pkl'.format(total_reward))
bestTotReward = total_reward
# plot cost and score
# plt.clf()
# plt.subplot(1, 2, 1)
# plt.plot(learner.get_cost_list(), '.')
# plt.subplot(1, 2, 2)
# sl = np.asarray(scoreList)
# plt.plot(sl, '.')
# plt.pause(0.0001)
# save params every 10 games
if episode % 10 == 0:
learner.save('dqn{0}.pkl'.format(episode))
et = time.time()
print("Episode " + str(episode) + " ended with score: " + str(total_reward))
print('Total Time:', et - st, 'Frame Count:', game_handler.total_frame_count, 'FPS:', game_handler.total_frame_count / (et - st))
# plt.ioff()
# plt.show()
# final save
learner.save('dqn{0}.pkl'.format(episode+1))
import matplotlib.pyplot as plt
import numpy as np
from learningALE.handlers.gamehandler import GameHandler
from learningALE.learners.DQN import DQNLearner
# setup vars
rom = b'D:\\_code\\breakout.bin'
gamename = 'breakout'
skip_frame = 4
num_actions = 4
learner = DQNLearner(skip_frame, num_actions)
epochs = 100
ep_count = 0
game_handler = GameHandler(rom, False, skip_frame, learner)
scoreList = list()
bestTotReward = -np.inf
plt.ion()
st = time.time()
while game_handler.total_frame_count/skip_frame < epochs*50000:
total_reward = game_handler.run_one_game(learner)
scoreList.append(total_reward)
learner.game_over()
# if this is the best score save it as such
if total_reward >= bestTotReward:
learner.save('dqnbest{0}.pkl'.format(total_reward))
bestTotReward = total_reward
import time
import matplotlib.pyplot as plt
import numpy as np
from learningALE.handlers.gamehandler import GameHandler
from DoubleDQNLearner import DoubleDQNLearner
from learningALE.tools.life_ram_inds import BREAKOUT
# setup vars
rom = b'D:\\_code\\_reinforcementlearning\\breakout.bin'
gamename = 'breakout'
skip_frame = 4
num_actions = 4
learner = DoubleDQNLearner(skip_frame, num_actions)
game_handler = GameHandler(rom, False, learner, skip_frame)
scoreList = list()
validLossList = list()
bestTotReward = -np.inf
plt.ion()
st = time.time()
for episode in range(5000):
learner.copy_new_target() # copy a new target learner at the start of each game
total_reward = game_handler.run_one_game(learner, lives=5, life_ram_ind=BREAKOUT)
scoreList.append(total_reward)
learner.game_over()
# if this is the best score save it as such
if total_reward >= bestTotReward:
learner.save('dqnbest{0}.pkl'.format(total_reward))
epoch_files.append(file)
epoch_dqn = list()
for dqn in epoch_files:
epoch = dqn.replace('dqn', '')
epoch = epoch.replace('.pkl', '')
epoch = round(float(epoch) / 50000, 1)
epoch_dqn.append([epoch, dqn])
epoch_dqn.sort()
# setup vars
rom = b'D:\\_code\\breakout.bin'
skip_frame = 4
num_actions = 4
game_handler = GameHandler(rom, False, skip_frame)
# now test to plot score
num_to_run = 10
means = list()
q_vals = list()
for ep, file in epoch_dqn:
player_file = os.getcwd() + '\\' + file
learner = DQNTester(skip_frame, num_actions, load=player_file)
game_handler.set_legal_actions(learner)
rews, times, frames = evaluate_player(learner, game_handler, num_to_run)
means.append(np.mean(rews))
qval_arr = np.asarray(learner.q_vals)
qval_arr[np.isinf(qval_arr)] = np.nan
mean_qvals = np.nanmean(qval_arr, axis=0)
import time
import matplotlib.pyplot as plt
import numpy as np
from learningALE.handlers.gamehandler import GameHandler
from learningALE.learners.DQN import DQNTester
# setup vars
rom = b'D:\\_code\\breakout.bin'
gamename = 'breakout'
skip_frame = 4
num_actions = 4
learner = DQNTester(skip_frame, num_actions,
load='D:\_code\learningALE\experiments\\reproduction\DQN_Original\dqnbest36.0.pkl',
rand_val=0.05)
st = time.time()
game_handler = GameHandler(rom, True, skip_frame, learner)
total_reward = game_handler.run_one_game(learner, clip=False, neg_reward=False)
et = time.time()
print("Episode ended with score: " + str(total_reward))
print('Total Time:', et - st, 'Frame Count:', game_handler.total_frame_count, 'FPS:', game_handler.total_frame_count / (et - st))
