def main():
parser = argparse.ArgumentParser(description='Script to test the trained network on a game.')
parser.add_argument('-r', '--rom', required=False, type=str,
default=os.path.join('roms', 'breakout.bin'),
help='Path of the ROM File.')
parser.add_argument('-v', '--visualization', required=False, type=int, default=0,
help='Visualize the runs.')
parser.add_argument('--lr', required=False, type=float, default=0.01,
help='Learning rate of the AdaGrad optimizer')
parser.add_argument('--eps', required=False, type=float, default=0.01,
help='Eps of the AdaGrad optimizer')
parser.add_argument('--clip-gradient', required=False, type=float, default=None,
help='Clip threshold of the AdaGrad optimizer')
parser.add_argument('--double-q', required=False, type=bool, default=False,
help='Use Double DQN')
parser.add_argument('--wd', required=False, type=float, default=0.0,
help='Weight of the L2 Regularizer')
parser.add_argument('-c', '--ctx', required=False, type=str, default='gpu',
help='Running Context. E.g `-c gpu` or `-c gpu1` or `-c cpu`')
parser.add_argument('-d', '--dir-path', required=False, type=str, default='',
help='Saving directory of model files.')
parser.add_argument('--start-eps', required=False, type=float, default=1.0,
help='Eps of the epsilon-greedy policy at the beginning')
parser.add_argument('--replay-start-size', required=False, type=int, default=50000,
help='The step that the training starts')
parser.add_argument('--kvstore-update-period', required=False, type=int, default=1,
help='The period that the worker updates the parameters from the sever')
parser.add_argument('--kv-type', required=False, type=str, default=None,
help='type of kvstore, default will not use kvstore, could also be dist_async')
parser.add_argument('--optimizer', required=False, type=str, default="adagrad",
help='type of optimizer')
args = parser.parse_args()
if args.dir_path == '':
rom_name = os.path.splitext(os.path.basename(args.rom))[0]
args.dir_path = 'dqn-%s-lr%g' % (rom_name, args.lr)
replay_start_size = args.replay_start_size
max_start_nullops = 30
replay_memory_size = 1000000
history_length = 4
rows = 84
cols = 84
ctx = parse_ctx(args.ctx)
q_ctx = mx.Context(*ctx[0])
game = AtariGame(rom_path=args.rom, resize_mode='scale', replay_start_size=replay_start_size,
resized_rows=rows, resized_cols=cols, max_null_op=max_start_nullops,
replay_memory_size=replay_memory_size, display_screen=args.visualization,
history_length=history_length)
##RUN NATURE
freeze_interval = 10000
epoch_num = 200
steps_per_epoch = 250000
update_interval = 4
discount = 0.99
eps_start = args.start_eps
eps_min = 0.1
eps_decay = (eps_start - eps_min) / 1000000
eps_curr = eps_start
freeze_interval /= update_interval
minibatch_size = 32
action_num = len(game.action_set)
data_shapes = {'data': (minibatch_size, history_length) + (rows, cols),
'dqn_action': (minibatch_size,), 'dqn_reward': (minibatch_size,)}
dqn_sym = dqn_sym_nature(action_num)
qnet = Base(data_shapes=data_shapes, sym_gen=dqn_sym, name='QNet',
initializer=DQNInitializer(factor_type="in"),
ctx=q_ctx)
target_qnet = qnet.copy(name="TargetQNet", ctx=q_ctx)
use_easgd = False
optimizer = mx.optimizer.create(name=args.optimizer, learning_rate=args.lr, eps=args.eps,
clip_gradient=args.clip_gradient,
rescale_grad=1.0, wd=args.wd)
updater = mx.optimizer.get_updater(optimizer)
qnet.print_stat()
target_qnet.print_stat()
# Begin Playing Game
training_steps = 0
total_steps = 0
for epoch in range(epoch_num):
# Run Epoch
steps_left = steps_per_epoch
episode = 0
epoch_reward = 0
start = time.time()
game.start()
while steps_left > 0:
# Running New Episode
episode += 1
episode_loss = 0.0
episode_q_value = 0.0
episode_update_step = 0
episode_action_step = 0
time_episode_start = time.time()
game.begin_episode(steps_left)
while not game.episode_terminate:
# 1. We need to choose a new action based on the current game status
if game.state_enabled and game.replay_memory.sample_enabled:
do_exploration = (npy_rng.rand() < eps_curr)
eps_curr = max(eps_curr - eps_decay, eps_min)
if do_exploration:
action = npy_rng.randint(action_num)
else:
# TODO Here we can in fact play multiple gaming instances simultaneously and make actions for each
# We can simply stack the current_state() of gaming instances and give prediction for all of them
# We need to wait after calling calc_score(.), which makes the program slow
# TODO Profiling the speed of this part!
current_state = game.current_state()
state = nd.array(current_state.reshape((1,) + current_state.shape),
ctx=q_ctx) / float(255.0)
qval_npy = qnet.forward(is_train=False, data=state)[0].asnumpy()
action = numpy.argmax(qval_npy)
episode_q_value += qval_npy[0, action]
episode_action_step += 1
else:
action = npy_rng.randint(action_num)
# 2. Play the game for a single mega-step (Inside the game, the action may be repeated for several times)
game.play(action)
total_steps += 1
# 3. Update our Q network if we can start sampling from the replay memory
# Also, we update every `update_interval`
if total_steps % update_interval == 0 and game.replay_memory.sample_enabled:
# 3.1 Draw sample from the replay_memory
training_steps += 1
episode_update_step += 1
states, actions, rewards, next_states, terminate_flags \
= game.replay_memory.sample(batch_size=minibatch_size)
states = nd.array(states, ctx=q_ctx) / float(255.0)
next_states = nd.array(next_states, ctx=q_ctx) / float(255.0)
actions = nd.array(actions, ctx=q_ctx)
rewards = nd.array(rewards, ctx=q_ctx)
terminate_flags = nd.array(terminate_flags, ctx=q_ctx)
# 3.2 Use the target network to compute the scores and
# get the corresponding target rewards
if not args.double_q:
target_qval = target_qnet.forward(is_train=False, data=next_states)[0]
target_rewards = rewards + nd.choose_element_0index(target_qval,
nd.argmax_channel(target_qval)) \
* (1.0 - terminate_flags) * discount
else:
target_qval = target_qnet.forward(is_train=False, data=next_states)[0]
qval = qnet.forward(is_train=False, data=next_states)[0]
target_rewards = rewards + nd.choose_element_0index(target_qval,
nd.argmax_channel(qval)) \
* (1.0 - terminate_flags) * discount
outputs = qnet.forward(is_train=True,
data=states,
dqn_action=actions,
dqn_reward=target_rewards)
qnet.backward()
qnet.update(updater=updater)
# 3.3 Calculate Loss
diff = nd.abs(nd.choose_element_0index(outputs[0], actions) - target_rewards)
quadratic_part = nd.clip(diff, -1, 1)
loss = 0.5 * nd.sum(nd.square(quadratic_part)).asnumpy()[0] + \
nd.sum(diff - quadratic_part).asnumpy()[0]
episode_loss += loss
# 3.3 Update the target network every freeze_interval
if training_steps % freeze_interval == 0:
qnet.copy_params_to(target_qnet)
steps_left -= game.episode_step
time_episode_end = time.time()
# Update the statistics
epoch_reward += game.episode_reward
info_str = "Epoch:%d, Episode:%d, Steps Left:%d/%d, Reward:%f, fps:%f, Exploration:%f" \
% (epoch, episode, steps_left, steps_per_epoch, game.episode_reward,
game.episode_step / (time_episode_end - time_episode_start), eps_curr)
if episode_update_step > 0:
info_str += ", Avg Loss:%f/%d" % (episode_loss / episode_update_step,
episode_update_step)
if episode_action_step > 0:
info_str += ", Avg Q Value:%f/%d" % (episode_q_value / episode_action_step,
episode_action_step)
if episode % 100 == 0:
logging.info(info_str)
end = time.time()
fps = steps_per_epoch / (end - start)
qnet.save_params(dir_path=args.dir_path, epoch=epoch)
logging.info("Epoch:%d, FPS:%f, Avg Reward: %f/%d"
% (epoch, fps, epoch_reward / float(episode), episode))
def main():
parser = argparse.ArgumentParser(description='Script to test the trained network on a game.')
parser.add_argument('-r', '--rom', required=False, type=str,
default=os.path.join('roms', 'breakout.bin'),
help='Path of the ROM File.')
parser.add_argument('-v', '--visualization', action='store_true',
help='Visualize the runs.')
parser.add_argument('--lr', required=False, type=float, default=0.01,
help='Learning rate of the AdaGrad optimizer')
parser.add_argument('--eps', required=False, type=float, default=0.01,
help='Eps of the AdaGrad optimizer')
parser.add_argument('--clip-gradient', required=False, type=float, default=None,
help='Clip threshold of the AdaGrad optimizer')
parser.add_argument('--double-q', action='store_true',
help='Use Double DQN only if specified')
parser.add_argument('--wd', required=False, type=float, default=0.0,
help='Weight of the L2 Regularizer')
parser.add_argument('-c', '--ctx', required=False, type=str, default='gpu',
help='Running Context. E.g `-c gpu` or `-c gpu1` or `-c cpu`')
parser.add_argument('-d', '--dir-path', required=False, type=str, default='',
help='Saving directory of model files.')
parser.add_argument('--start-eps', required=False, type=float, default=1.0,
help='Eps of the epsilon-greedy policy at the beginning')
parser.add_argument('--replay-start-size', required=False, type=int, default=50000,
help='The step that the training starts')
parser.add_argument('--kvstore-update-period', required=False, type=int, default=1,
help='The period that the worker updates the parameters from the sever')
parser.add_argument('--kv-type', required=False, type=str, default=None,
help='type of kvstore, default will not use kvstore, could also be dist_async')
parser.add_argument('--optimizer', required=False, type=str, default="adagrad",
help='type of optimizer')
args = parser.parse_args()
if args.dir_path == '':
rom_name = os.path.splitext(os.path.basename(args.rom))[0]
args.dir_path = 'dqn-%s-lr%g' % (rom_name, args.lr)
replay_start_size = args.replay_start_size
max_start_nullops = 30
replay_memory_size = 1000000
history_length = 4
rows = 84
cols = 84
ctx = parse_ctx(args.ctx)
q_ctx = mx.Context(*ctx[0])
game = AtariGame(rom_path=args.rom, resize_mode='scale', replay_start_size=replay_start_size,
resized_rows=rows, resized_cols=cols, max_null_op=max_start_nullops,
replay_memory_size=replay_memory_size, display_screen=args.visualization,
history_length=history_length)
##RUN NATURE
freeze_interval = 10000
epoch_num = 200
steps_per_epoch = 250000
update_interval = 4
discount = 0.99
eps_start = args.start_eps
eps_min = 0.1
eps_decay = (eps_start - eps_min) / 1000000
eps_curr = eps_start
freeze_interval /= update_interval
minibatch_size = 32
action_num = len(game.action_set)
data_shapes = {'data': (minibatch_size, history_length) + (rows, cols),
'dqn_action': (minibatch_size,), 'dqn_reward': (minibatch_size,)}
dqn_sym = dqn_sym_nature(action_num)
qnet = Base(data_shapes=data_shapes, sym_gen=dqn_sym, name='QNet',
initializer=DQNInitializer(factor_type="in"),
ctx=q_ctx)
target_qnet = qnet.copy(name="TargetQNet", ctx=q_ctx)
use_easgd = False
optimizer = mx.optimizer.create(name=args.optimizer, learning_rate=args.lr, eps=args.eps,
clip_gradient=args.clip_gradient,
rescale_grad=1.0, wd=args.wd)
updater = mx.optimizer.get_updater(optimizer)
qnet.print_stat()
target_qnet.print_stat()
# Begin Playing Game
training_steps = 0
total_steps = 0
for epoch in range(epoch_num):
# Run Epoch
steps_left = steps_per_epoch
episode = 0
epoch_reward = 0
start = time.time()
game.start()
while steps_left > 0:
# Running New Episode
episode += 1
episode_loss = 0.0
episode_q_value = 0.0
episode_update_step = 0
episode_action_step = 0
time_episode_start = time.time()
game.begin_episode(steps_left)
while not game.episode_terminate:
# 1. We need to choose a new action based on the current game status
if game.state_enabled and game.replay_memory.sample_enabled:
do_exploration = (npy_rng.rand() < eps_curr)
eps_curr = max(eps_curr - eps_decay, eps_min)
if do_exploration:
action = npy_rng.randint(action_num)
else:
# TODO Here we can in fact play multiple gaming instances simultaneously and make actions for each
# We can simply stack the current_state() of gaming instances and give prediction for all of them
# We need to wait after calling calc_score(.), which makes the program slow
# TODO Profiling the speed of this part!
current_state = game.current_state()
state = nd.array(current_state.reshape((1,) + current_state.shape),
ctx=q_ctx) / float(255.0)
qval_npy = qnet.forward(is_train=False, data=state)[0].asnumpy()
action = numpy.argmax(qval_npy)
episode_q_value += qval_npy[0, action]
episode_action_step += 1
else:
action = npy_rng.randint(action_num)
# 2. Play the game for a single mega-step (Inside the game, the action may be repeated for several times)
game.play(action)
total_steps += 1
# 3. Update our Q network if we can start sampling from the replay memory
# Also, we update every `update_interval`
if total_steps % update_interval == 0 and game.replay_memory.sample_enabled:
# 3.1 Draw sample from the replay_memory
training_steps += 1
episode_update_step += 1
states, actions, rewards, next_states, terminate_flags \
= game.replay_memory.sample(batch_size=minibatch_size)
states = nd.array(states, ctx=q_ctx) / float(255.0)
next_states = nd.array(next_states, ctx=q_ctx) / float(255.0)
actions = nd.array(actions, ctx=q_ctx)
rewards = nd.array(rewards, ctx=q_ctx)
terminate_flags = nd.array(terminate_flags, ctx=q_ctx)
# 3.2 Use the target network to compute the scores and
# get the corresponding target rewards
if not args.double_q:
target_qval = target_qnet.forward(is_train=False, data=next_states)[0]
target_rewards = rewards + nd.choose_element_0index(target_qval,
nd.argmax_channel(target_qval))\
* (1.0 - terminate_flags) * discount
else:
target_qval = target_qnet.forward(is_train=False, data=next_states)[0]
qval = qnet.forward(is_train=False, data=next_states)[0]
target_rewards = rewards + nd.choose_element_0index(target_qval,
nd.argmax_channel(qval))\
* (1.0 - terminate_flags) * discount
outputs = qnet.forward(is_train=True,
data=states,
dqn_action=actions,
dqn_reward=target_rewards)
qnet.backward()
qnet.update(updater=updater)
# 3.3 Calculate Loss
diff = nd.abs(nd.choose_element_0index(outputs[0], actions) - target_rewards)
quadratic_part = nd.clip(diff, -1, 1)
loss = 0.5 * nd.sum(nd.square(quadratic_part)).asnumpy()[0] +\
nd.sum(diff - quadratic_part).asnumpy()[0]
episode_loss += loss
# 3.3 Update the target network every freeze_interval
if training_steps % freeze_interval == 0:
qnet.copy_params_to(target_qnet)
steps_left -= game.episode_step
time_episode_end = time.time()
# Update the statistics
epoch_reward += game.episode_reward
info_str = "Epoch:%d, Episode:%d, Steps Left:%d/%d, Reward:%f, fps:%f, Exploration:%f" \
% (epoch, episode, steps_left, steps_per_epoch, game.episode_reward,
game.episode_step / (time_episode_end - time_episode_start), eps_curr)
if episode_update_step > 0:
info_str += ", Avg Loss:%f/%d" % (episode_loss / episode_update_step,
episode_update_step)
if episode_action_step > 0:
info_str += ", Avg Q Value:%f/%d" % (episode_q_value / episode_action_step,
episode_action_step)
if episode % 100 == 0:
logging.info(info_str)
end = time.time()
fps = steps_per_epoch / (end - start)
qnet.save_params(dir_path=args.dir_path, epoch=epoch)
logging.info("Epoch:%d, FPS:%f, Avg Reward: %f/%d"
% (epoch, fps, epoch_reward / float(episode), episode))
