def main(_):
with tf.Session() as sess:
config = get_config(FLAGS) or FLAGS
if config.env_type == 'simple':
env = SimpleGymEnvironment(config)
else:
env = GymEnvironment(config)
if not tf.test.is_gpu_available() and FLAGS.use_gpu:
raise Exception("use_gpu flag is true when no GPUs are available")
if not FLAGS.use_gpu:
config.cnn_format = 'NHWC'
roms = 'roms/Pong2PlayerVS.bin'
ale = ALEInterface(roms.encode('utf-8'))
width = ale.ale_getScreenWidth()
height = ale.ale_getScreenHeight()
game_screen = GameScreen()
ale.ale_resetGame()
(display_width, display_height) = (width * 2, height * 2)
pygame.init()
screen_ale = pygame.display.set_mode((display_width, display_height))
pygame.display.set_caption("Arcade Learning Environment Random Agent Display")
pygame.display.flip()
game_surface = pygame.Surface((width, height), depth=8)
clock = pygame.time.Clock()
# Clear screen
screen_ale.fill((0, 0, 0))
agent = Agent(config, env, sess, 'A')
agent2 = Agent2(config, env, sess, 'B')
if FLAGS.is_train:
start_epoch = agent.epoch_op.eval()
start_step = agent.step_op.eval()
start_time = time.time()
# Loop for epochs
for agent.epoch in range(start_epoch, agent.max_epoch):
agent2.epoch = agent.epoch
# Initialize information of gameplay
num_game, agent.update_count, agent2.update_count, ep_rewardA, ep_rewardB = 0, 0, 0, 0., 0.
total_rewardA, total_rewardB, agent.total_loss, agent2.total_loss, agent.total_q, agent2.total_q = 0., 0., 0., 0., 0., 0.
max_avg_ep_rewardA, max_avg_ep_rewardB = 0, 0
ep_rewardsA, ep_rewardsB, actionsA, actionsB = [], [], [], []
# Get first frame of gameplay
numpy_surface = np.frombuffer(game_surface.get_buffer(), dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
# Add first frame of gameplay into both agents' replay history
for _ in range(agent.history_length):
agent.history.add(scaled_pooled_screen)
agent2.history.add(scaled_pooled_screen)
# Loop for training iterations
for agent.step in tqdm(range(start_step, agent.max_step), ncols=70, initial=start_step):
agent2.step = agent.step
# End of burn in period, start to learn from frames
if agent.step == agent.learn_start:
num_game, agent.update_count, agent2.update_count, ep_rewardA, ep_rewardB = 0, 0, 0, 0., 0.
total_rewardA, total_rewardB, agent.total_loss, agent2.total_loss, agent.total_q, agent2.total_q = 0., 0., 0., 0., 0., 0.
max_avg_ep_rewardA, max_avg_ep_rewardB = 0, 0
ep_rewardsA, ep_rewardsB, actionsA, actionsB = [], [], [], []
# 1. predict
action1 = agent.predict(agent.history.get())
action2 = agent2.predict(agent2.history.get())
# 2. act
ale.ale_act2(action1, action2)
terminal = ale.ale_isGameOver()
# End of end epoch, finish up training so that game statistics can be collected without training data being messed up
if agent.step == agent.max_step - 1:
terminal = True
rewardA = ale.ale_getRewardA()
rewardB = ale.ale_getRewardB()
# Fill buffer of game screen with current frame
numpy_surface = np.frombuffer(game_surface.get_buffer(), dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
agent.observe(scaled_pooled_screen, rewardA, action1, terminal)
agent2.observe(scaled_pooled_screen, rewardB, action2, terminal)
# Print frame onto display screen
screen_ale.blit(pygame.transform.scale2x(game_surface), (0, 0))
# Update the display screen
pygame.display.flip()
# Check if current episode ended
if terminal:
ale.ale_resetGame()
terminal = ale.ale_isGameOver()
rewardA = ale.ale_getRewardA()
rewardB = ale.ale_getRewardB()
numpy_surface = np.frombuffer(game_surface.get_buffer(), dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
# End of an episode
num_game += 1
ep_rewardsA.append(ep_rewardA)
ep_rewardsB.append(ep_rewardB)
ep_rewardA = 0.
ep_rewardB = 0.
else:
ep_rewardA += rewardA
ep_rewardB += rewardB
actionsA.append(action1)
actionsB.append(action2)
total_rewardA += rewardA
total_rewardB += rewardB
# Do a test to get statistics so far
if agent.step >= agent.learn_start:
if agent.step % agent.test_step == agent.test_step - 1:
avg_rewardA = total_rewardA / agent.test_step
avg_rewardB = total_rewardB / agent2.test_step
avg_lossA = agent.total_loss / agent.update_count
avg_lossB = agent2.total_loss / agent2.update_count
avg_qA = agent.total_q / agent.update_count
avg_qB = agent2.total_q / agent2.update_count
try:
max_ep_rewardA = np.max(ep_rewardsA)
min_ep_rewardA = np.min(ep_rewardsA)
avg_ep_rewardA = np.mean(ep_rewardsA)
max_ep_rewardB = np.max(ep_rewardsB)
min_ep_rewardB = np.min(ep_rewardsB)
avg_ep_rewardB = np.mean(ep_rewardsB)
except:
max_ep_rewardA, min_ep_rewardA, avg_ep_rewardA, max_ep_rewardB, min_ep_rewardB, avg_ep_rewardB = 0, 0, 0, 0, 0, 0
print('\nFor Agent A at Epoch %d: avg_r: %.4f, avg_l: %.6f, avg_q: %3.6f, avg_ep_r: %.4f, max_ep_r: %.4f, min_ep_r: %.4f, # game: %d' \
% (agent.epoch, avg_rewardA, avg_lossA, avg_qA, avg_ep_rewardA, max_ep_rewardA, min_ep_rewardA, num_game))
print('\nFor Agent B at Epoch %d: avg_r: %.4f, avg_l: %.6f, avg_q: %3.6f, avg_ep_r: %.4f, max_ep_r: %.4f, min_ep_r: %.4f, # game: %d' \
% (agent2.epoch, avg_rewardB, avg_lossB, avg_qB, avg_ep_rewardB, max_ep_rewardB, min_ep_rewardB, num_game))
if max_avg_ep_rewardA * 0.9 <= avg_ep_rewardA:
agent.step_assign_op.eval({agent.step_input: agent.step + 1})
agent.save_model(agent.step + 1)
max_avg_ep_rewardA = max(max_avg_ep_rewardA, avg_ep_rewardA)
if max_avg_ep_rewardB * 0.9 <= avg_ep_rewardB:
agent2.step_assign_op.eval({agent2.step_input: agent2.step + 1})
agent2.save_model(agent2.step + 1)
max_avg_ep_rewardB = max(max_avg_ep_rewardB, avg_ep_rewardB)
if agent.step > 180:
agent.inject_summary({
'average.reward': avg_rewardA,
'average.loss': avg_lossA,
'average.q': avg_qA,
'episode.max reward': max_ep_rewardA,
'episode.min reward': min_ep_rewardA,
'episode.avg reward': avg_ep_rewardA,
'episode.num of game': num_game,
'episode.rewards': ep_rewardsA,
'episode.actions': actionsA,
'training.learning_rate': agent.learning_rate_op.eval({agent.learning_rate_step: agent.step}),
}, agent.step)
if agent2.step > 180:
agent2.inject_summary({
'average.reward': avg_rewardB,
'average.loss': avg_lossB,
'average.q': avg_qB,
'episode.max reward': max_ep_rewardB,
'episode.min reward': min_ep_rewardB,
'episode.avg reward': avg_ep_rewardB,
'episode.num of game': num_game,
'episode.rewards': ep_rewardsB,
'episode.actions': actionsB,
'training.learning_rate': agent2.learning_rate_op.eval({agent2.learning_rate_step: agent2.step}),
}, agent2.step)
# Reset statistics
num_game = 0
total_rewardA, total_rewardB = 0., 0.
agent.total_loss, agent2.total_loss = 0., 0.
agent.total_q, agent2.total_q = 0., 0.
agent.update_count, agent2.update_count = 0, 0
ep_rewardA, ep_rewardB = 0., 0.
ep_rewardsA, ep_rewardsB = [], []
actionsA, actionsB = [], []
# Play 10 games at the end of epoch to get game statistics
total_points, paddle_bounce, wall_bounce, serving_time = [], [], [], []
for _ in range(10):
cur_total_points, cur_paddle_bounce, cur_wall_bounce, cur_serving_time = 0, 0, 0, 0
# Restart game
ale.ale_resetGame()
# Get first frame of gameplay
numpy_surface = np.frombuffer(game_surface.get_buffer(), dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
# Create history for testing purposes
test_history = History(config)
# Fill first 4 images with initial screen
for _ in range(agent.history_length):
test_history.add(scaled_pooled_screen)
while not ale.ale_isGameOver():
# 1. predict
action1 = agent.predict(agent.history.get())
action2 = agent2.predict(agent2.history.get())
# 2. act
ale.ale_act2(action1, action2)
terminal = ale.ale_isGameOver()
rewardA = ale.ale_getRewardA()
rewardB = ale.ale_getRewardB()
# Record game statistics of current episode
cur_total_points = ale.ale_getPoints()
cur_paddle_bounce = ale.ale_getSideBouncing()
if ale.ale_getWallBouncing():
cur_wall_bounce += 1
if ale.ale_getServing():
cur_serving_time += 1
# Fill buffer of game screen with current frame
numpy_surface = np.frombuffer(game_surface.get_buffer(), dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
agent.observe(scaled_pooled_screen, rewardA, action1, terminal)
agent2.observe(scaled_pooled_screen, rewardB, action2, terminal)
# Print frame onto display screen
screen_ale.blit(pygame.transform.scale2x(game_surface), (0, 0))
# Update the display screen
pygame.display.flip()
# Append current episode's statistics into list
total_points.append(cur_total_points)
paddle_bounce.append(cur_paddle_bounce / cur_total_points)
if cur_paddle_bounce == 0:
wall_bounce.append(cur_wall_bounce / (cur_paddle_bounce + 1))
else:
wall_bounce.append(cur_wall_bounce / cur_paddle_bounce)
serving_time.append(cur_serving_time / cur_total_points)
# Save results of test after current epoch
cur_paddle_op = agent.paddle_op.eval()
cur_paddle_op[agent.epoch] = sum(paddle_bounce) / len(paddle_bounce)
agent.paddle_assign_op.eval({agent.paddle_input: cur_paddle_op})
cur_wall_op = agent.wall_op.eval()
cur_wall_op[agent.epoch] = sum(wall_bounce) / len(wall_bounce)
agent.wall_assign_op.eval({agent.wall_input: cur_wall_op})
cur_serving_op = agent.serving_op.eval()
cur_serving_op[agent.epoch] = sum(serving_time) / len(serving_time)
agent.serving_assign_op.eval({agent.serving_input: cur_serving_op})
agent.save_model(agent.step + 1)
else:
agent.play()
agent2.play()
def main(_):
with tf.Session() as sess:
config = get_config(FLAGS) or FLAGS
if config.env_type == 'simple':
env = SimpleGymEnvironment(config)
else:
env = GymEnvironment(config)
if not tf.test.is_gpu_available() and FLAGS.use_gpu:
raise Exception("use_gpu flag is true when no GPUs are available")
if not FLAGS.use_gpu:
config.cnn_format = 'NHWC'
roms = 'roms/Pong2PlayerVS.bin'
ale = ALEInterface(roms.encode('utf-8'))
width = ale.ale_getScreenWidth()
height = ale.ale_getScreenHeight()
game_screen = GameScreen()
ale.ale_resetGame()
(display_width, display_height) = (width * 2, height * 2)
pygame.init()
screen_ale = pygame.display.set_mode((display_width, display_height))
pygame.display.set_caption(
"Arcade Learning Environment Random Agent Display")
pygame.display.flip()
game_surface = pygame.Surface((width, height), depth=8)
clock = pygame.time.Clock()
# Clear screen
screen_ale.fill((0, 0, 0))
agent = Agent(config, env, sess)
if FLAGS.is_train:
start_step = agent.step_op.eval()
start_time = time.time()
num_game, agent.update_count, ep_reward = 0, 0, 0.
total_reward, agent.total_loss, agent.total_q = 0., 0., 0.
max_avg_ep_reward = 0
ep_rewards, actions = [], []
numpy_surface = np.frombuffer(game_surface.get_buffer(),
dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
for _ in range(agent.history_length):
agent.history.add(scaled_pooled_screen)
for agent.step in tqdm(range(start_step, agent.max_step),
ncols=70,
initial=start_step):
if agent.step == agent.learn_start:
num_game, agent.update_count, ep_reward = 0, 0, 0.
total_reward, agent.total_loss, agent.total_q = 0., 0., 0.
ep_rewards, actions = [], []
# 1. predict
action = agent.predict(agent.history.get())
# 2. act
ale.ale_act2(action, np.random.choice([20, 21, 23, 24]))
terminal = ale.ale_isGameOver()
reward = ale.ale_getRewardA()
# screen, reward, terminal = agent.env.act(action, is_training=True)
# 3. observe
# Both agents perform random actions
# Agent A : [NOOP, FIRE, RIGHT, LEFT]
# Agent B : [NOOP, FIRE, RIGHT, LEFT]
# Fill buffer of game screen with current frame
numpy_surface = np.frombuffer(game_surface.get_buffer(),
dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
agent.observe(scaled_pooled_screen, reward, action, terminal)
# Print frame onto display screen
screen_ale.blit(pygame.transform.scale2x(game_surface), (0, 0))
#Update the display screen
pygame.display.flip()
if terminal:
ale.ale_resetGame()
terminal = ale.ale_isGameOver()
reward = ale.ale_getRewardA()
numpy_surface = np.frombuffer(game_surface.get_buffer(),
dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
num_game += 1
ep_rewards.append(ep_reward)
ep_reward = 0.
else:
ep_reward += reward
actions.append(action)
total_reward += reward
if agent.step >= agent.learn_start:
if agent.step % agent.test_step == agent.test_step - 1:
avg_reward = total_reward / agent.test_step
avg_loss = agent.total_loss / agent.update_count
avg_q = agent.total_q / agent.update_count
try:
max_ep_reward = np.max(ep_rewards)
min_ep_reward = np.min(ep_rewards)
avg_ep_reward = np.mean(ep_rewards)
except:
max_ep_reward, min_ep_reward, avg_ep_reward = 0, 0, 0
print('\navg_r: %.4f, avg_l: %.6f, avg_q: %3.6f, avg_ep_r: %.4f, max_ep_r: %.4f, min_ep_r: %.4f, # game: %d' \
% (avg_reward, avg_loss, avg_q, avg_ep_reward, max_ep_reward, min_ep_reward, num_game))
if max_avg_ep_reward * 0.9 <= avg_ep_reward:
agent.step_assign_op.eval(
{agent.step_input: agent.step + 1})
agent.save_model(agent.step + 1)
max_avg_ep_reward = max(max_avg_ep_reward,
avg_ep_reward)
if agent.step > 180:
agent.inject_summary(
{
'average.reward':
avg_reward,
'average.loss':
avg_loss,
'average.q':
avg_q,
'episode.max reward':
max_ep_reward,
'episode.min reward':
min_ep_reward,
'episode.avg reward':
avg_ep_reward,
'episode.num of game':
num_game,
'episode.rewards':
ep_rewards,
'episode.actions':
actions,
'training.learning_rate':
agent.learning_rate_op.eval(
{agent.learning_rate_step: agent.step
}),
}, agent.step)
num_game = 0
total_reward = 0.
agent.total_loss = 0.
agent.total_q = 0.
agent.update_count = 0
ep_reward = 0.
ep_rewards = []
actions = []
else:
while not ale.ale_isGameOver():
# Fill buffer of game screen with current frame
numpy_surface = np.frombuffer(game_surface.get_buffer(),
dtype=np.uint8)
rgb = getRgbFromPalette(ale, game_surface, numpy_surface)
del numpy_surface
game_screen.paint(rgb)
pooled_screen = game_screen.grab()
scaled_pooled_screen = scale_image(pooled_screen)
ale.ale_act2(agent.predict(pooled_screen),
np.random.choice([20, 21, 23, 24]))
print(ale.ale_getRewardA())
# Print frame onto display screen
screen.blit(pygame.transform.scale2x(game_surface), (0, 0))
# Update the display screen
pygame.display.flip()
# delay to 60fps
clock.tick(60.)
