def main():
env = get_player(
args.rom, image_size=IMAGE_SIZE, train=True, frame_skip=FRAME_SKIP)
file_path = "memory.npz"
rpm = ReplayMemory(
MEMORY_SIZE,
IMAGE_SIZE,
CONTEXT_LEN,
load_file=True, # load replay memory data from file
file_path=file_path)
act_dim = env.action_space.n
model = AtariModel(act_dim)
algorithm = DQN(
model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE * gpu_num)
agent = AtariAgent(
algorithm, act_dim=act_dim, total_step=args.train_total_steps)
if os.path.isfile('./model.ckpt'):
logger.info("load model from file")
agent.restore('./model.ckpt')
if args.train:
logger.info("train with memory data")
run_train_step(agent, rpm)
logger.info("finish training. Save the model.")
agent.save('./model.ckpt')
else:
logger.info("collect experience")
collect_exp(env, rpm, agent)
rpm.save_memory()
logger.info("finish collecting, save successfully")
def __init__(self, config):
self.config = config
self.sample_data_queue = queue.Queue(
maxsize=config['sample_queue_max_size'])
#=========== Create Agent ==========
env = gym.make(config['env_name'])
env = wrap_deepmind(env, dim=config['env_dim'], obs_format='NCHW')
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
model = AtariModel(act_dim)
algorithm = parl.algorithms.IMPALA(
model,
sample_batch_steps=self.config['sample_batch_steps'],
gamma=self.config['gamma'],
vf_loss_coeff=self.config['vf_loss_coeff'],
clip_rho_threshold=self.config['clip_rho_threshold'],
clip_pg_rho_threshold=self.config['clip_pg_rho_threshold'])
self.agent = AtariAgent(algorithm, obs_shape, act_dim,
self.learn_data_provider)
if machine_info.is_gpu_available():
assert get_gpu_count() == 1, 'Only support training in single GPU,\
Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_TO_USE]` .'
self.cache_params = self.agent.get_weights()
self.params_lock = threading.Lock()
self.params_updated = False
self.cache_params_sent_cnt = 0
self.total_params_sync = 0
#========== Learner ==========
self.lr, self.entropy_coeff = None, None
self.lr_scheduler = PiecewiseScheduler(config['lr_scheduler'])
self.entropy_coeff_scheduler = PiecewiseScheduler(
config['entropy_coeff_scheduler'])
self.total_loss_stat = WindowStat(100)
self.pi_loss_stat = WindowStat(100)
self.vf_loss_stat = WindowStat(100)
self.entropy_stat = WindowStat(100)
self.kl_stat = WindowStat(100)
self.learn_time_stat = TimeStat(100)
self.start_time = None
self.learn_thread = threading.Thread(target=self.run_learn)
self.learn_thread.setDaemon(True)
self.learn_thread.start()
#========== Remote Actor ===========
self.remote_count = 0
self.batch_buffer = []
self.remote_metrics_queue = queue.Queue()
self.sample_total_steps = 0
self.create_actors()
def main():
env = get_player(args.rom,
image_size=IMAGE_SIZE,
train=True,
frame_skip=FRAME_SKIP)
test_env = get_player(args.rom,
image_size=IMAGE_SIZE,
frame_skip=FRAME_SKIP,
context_len=CONTEXT_LEN)
rpm = ReplayMemory(MEMORY_SIZE, IMAGE_SIZE, CONTEXT_LEN)
act_dim = env.action_space.n
model = AtariModel(act_dim, args.algo)
if args.algo == 'DDQN':
algorithm = parl.algorithms.DDQN(model, act_dim=act_dim, gamma=GAMMA)
elif args.algo in ['DQN', 'Dueling']:
algorithm = parl.algorithms.DQN(model, act_dim=act_dim, gamma=GAMMA)
agent = AtariAgent(algorithm,
act_dim=act_dim,
start_lr=LEARNING_RATE,
total_step=args.train_total_steps,
update_freq=UPDATE_FREQ)
with tqdm(total=MEMORY_WARMUP_SIZE,
desc='[Replay Memory Warm Up]') as pbar:
while rpm.size() < MEMORY_WARMUP_SIZE:
total_reward, steps, _ = run_train_episode(env, agent, rpm)
pbar.update(steps)
# train
test_flag = 0
pbar = tqdm(total=args.train_total_steps)
total_steps = 0
max_reward = None
while total_steps < args.train_total_steps:
# start epoch
total_reward, steps, loss = run_train_episode(env, agent, rpm)
total_steps += steps
pbar.set_description('[train]exploration:{}'.format(agent.exploration))
summary.add_scalar('dqn/score', total_reward, total_steps)
summary.add_scalar('dqn/loss', loss, total_steps) # mean of total loss
summary.add_scalar('dqn/exploration', agent.exploration, total_steps)
pbar.update(steps)
if total_steps // args.test_every_steps >= test_flag:
while total_steps // args.test_every_steps >= test_flag:
test_flag += 1
pbar.write("testing")
eval_rewards = []
for _ in tqdm(range(3), desc='eval agent'):
eval_reward = run_evaluate_episode(test_env, agent)
eval_rewards.append(eval_reward)
logger.info(
"eval_agent done, (steps, eval_reward): ({}, {})".format(
total_steps, np.mean(eval_rewards)))
eval_test = np.mean(eval_rewards)
summary.add_scalar('dqn/eval', eval_test, total_steps)
pbar.close()
def __init__(self, config):
self.config = config
#=========== Create Agent ==========
env = gym.make(config['env_name'])
env = wrap_deepmind(env, dim=config['env_dim'], obs_format='NCHW')
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
self.config['obs_shape'] = obs_shape
self.config['act_dim'] = act_dim
model = AtariModel(act_dim)
algorithm = parl.algorithms.A3C(model,
vf_loss_coeff=config['vf_loss_coeff'])
self.agent = AtariAgent(algorithm, config)
if machine_info.is_gpu_available():
assert get_gpu_count() == 1, 'Only support training in single GPU,\
Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_TO_USE]` .'
#========== Learner ==========
self.total_loss_stat = WindowStat(100)
self.pi_loss_stat = WindowStat(100)
self.vf_loss_stat = WindowStat(100)
self.entropy_stat = WindowStat(100)
self.lr = None
self.entropy_coeff = None
self.learn_time_stat = TimeStat(100)
self.start_time = None
#========== Remote Actor ===========
self.remote_count = 0
self.sample_data_queue = queue.Queue()
self.remote_metrics_queue = queue.Queue()
self.sample_total_steps = 0
self.params_queues = []
self.create_actors()
def __init__(self, config):
self.config = config
self.envs = []
for _ in range(config['env_num']):
env = gym.make(config['env_name'])
env = wrap_deepmind(env, dim=config['env_dim'], obs_format='NCHW')
self.envs.append(env)
self.vector_env = VectorEnv(self.envs)
self.obs_batch = self.vector_env.reset()
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
self.config['obs_shape'] = obs_shape
self.config['act_dim'] = act_dim
model = AtariModel(act_dim)
algorithm = parl.algorithms.A3C(model,
vf_loss_coeff=config['vf_loss_coeff'])
self.agent = AtariAgent(algorithm, config)
def __init__(self, config):
self.config = config
self.envs = []
for _ in range(config['env_num']):
env = gym.make(config['env_name'])
env = wrap_deepmind(env, dim=config['env_dim'], obs_format='NCHW')
self.envs.append(env)
self.vector_env = VectorEnv(self.envs)
self.obs_batch = self.vector_env.reset()
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
model = AtariModel(act_dim)
algorithm = parl.algorithms.IMPALA(
model,
sample_batch_steps=self.config['sample_batch_steps'],
gamma=self.config['gamma'],
vf_loss_coeff=self.config['vf_loss_coeff'],
clip_rho_threshold=self.config['clip_rho_threshold'],
clip_pg_rho_threshold=self.config['clip_pg_rho_threshold'])
self.agent = AtariAgent(algorithm, obs_shape, act_dim)
def main():
# Prepare environments
env = get_player(
args.rom, image_size=IMAGE_SIZE, train=True, frame_skip=FRAME_SKIP)
test_env = get_player(
args.rom,
image_size=IMAGE_SIZE,
frame_skip=FRAME_SKIP,
context_len=CONTEXT_LEN)
# Init Prioritized Replay Memory
per = ProportionalPER(alpha=0.6, seg_num=args.batch_size, size=MEMORY_SIZE)
# Prepare PARL agent
act_dim = env.action_space.n
model = AtariModel(act_dim)
if args.alg == 'ddqn':
algorithm = PrioritizedDoubleDQN(
model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE)
elif args.alg == 'dqn':
algorithm = PrioritizedDQN(
model, act_dim=act_dim, gamma=GAMMA, lr=LEARNING_RATE)
agent = AtariAgent(algorithm, act_dim=act_dim, update_freq=UPDATE_FREQ)
# Replay memory warmup
total_step = 0
with tqdm(total=MEMORY_SIZE, desc='[Replay Memory Warm Up]') as pbar:
mem = []
while total_step < MEMORY_WARMUP_SIZE:
total_reward, steps, _ = run_episode(
env, agent, per, mem=mem, warmup=True)
total_step += steps
pbar.update(steps)
per.elements.from_list(mem[:int(MEMORY_WARMUP_SIZE)])
env_name = args.rom.split('/')[-1].split('.')[0]
test_flag = 0
total_steps = 0
pbar = tqdm(total=args.train_total_steps)
while total_steps < args.train_total_steps:
# start epoch
total_reward, steps, loss = run_episode(env, agent, per, train=True)
total_steps += steps
pbar.set_description('[train]exploration:{}'.format(agent.exploration))
summary.add_scalar('{}/score'.format(env_name), total_reward,
total_steps)
summary.add_scalar('{}/loss'.format(env_name), loss,
total_steps) # mean of total loss
summary.add_scalar('{}/exploration'.format(env_name),
agent.exploration, total_steps)
pbar.update(steps)
if total_steps // args.test_every_steps >= test_flag:
while total_steps // args.test_every_steps >= test_flag:
test_flag += 1
pbar.write("testing")
test_rewards = []
for _ in tqdm(range(3), desc='eval agent'):
eval_reward = run_evaluate_episode(test_env, agent)
test_rewards.append(eval_reward)
eval_reward = np.mean(test_rewards)
logger.info(
"eval_agent done, (steps, eval_reward): ({}, {})".format(
total_steps, eval_reward))
summary.add_scalar('{}/eval'.format(env_name), eval_reward,
total_steps)
pbar.close()
def __init__(self, config):
self.config = config
self.sample_data_queue = queue.Queue()
self.batch_buffer = defaultdict(list)
#=========== Create Agent ==========
env = gym.make(config['env_name'])
env = wrap_deepmind(env, dim=config['env_dim'], obs_format='NCHW')
obs_shape = env.observation_space.shape
act_dim = env.action_space.n
self.config['obs_shape'] = obs_shape
self.config['act_dim'] = act_dim
model = AtariModel(act_dim)
algorithm = parl.algorithms.A3C(model,
vf_loss_coeff=config['vf_loss_coeff'])
self.agent = AtariAgent(
algorithm,
obs_shape=self.config['obs_shape'],
predict_thread_num=self.config['predict_thread_num'],
learn_data_provider=self.learn_data_provider)
if machine_info.is_gpu_available():
assert get_gpu_count() == 1, 'Only support training in single GPU,\
Please set environment variable: `export CUDA_VISIBLE_DEVICES=[GPU_ID_YOU_WANT_TO_USE]` .'
else:
cpu_num = os.environ.get('CPU_NUM')
assert cpu_num is not None and cpu_num == '1', 'Only support training in single CPU,\
Please set environment variable: `export CPU_NUM=1`.'
#========== Learner ==========
self.lr, self.entropy_coeff = None, None
self.lr_scheduler = PiecewiseScheduler(config['lr_scheduler'])
self.entropy_coeff_scheduler = PiecewiseScheduler(
config['entropy_coeff_scheduler'])
self.total_loss_stat = WindowStat(100)
self.pi_loss_stat = WindowStat(100)
self.vf_loss_stat = WindowStat(100)
self.entropy_stat = WindowStat(100)
self.learn_time_stat = TimeStat(100)
self.start_time = None
# learn thread
self.learn_thread = threading.Thread(target=self.run_learn)
self.learn_thread.setDaemon(True)
self.learn_thread.start()
self.predict_input_queue = queue.Queue()
# predict thread
self.predict_threads = []
for i in six.moves.range(self.config['predict_thread_num']):
predict_thread = threading.Thread(target=self.run_predict,
args=(i, ))
predict_thread.setDaemon(True)
predict_thread.start()
self.predict_threads.append(predict_thread)
#========== Remote Simulator ===========
self.remote_count = 0
self.remote_metrics_queue = queue.Queue()
self.sample_total_steps = 0
self.create_actors()
break
return total_reward
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--game_name', default='Phoenix-v0')
test_env = get_player('Phoenix-v0',
image_size=IMAGE_SIZE,
context_len=CONTEXT_LEN)
save_path = './dqn_model.ckpt'
act_dim = test_env.action_space.n
model = AtariModel(act_dim)
algorithm = parl.algorithms.DQN(model, act_dim=act_dim, gamma=GAMMA)
agent = AtariAgent(algorithm,
act_dim=act_dim,
start_lr=LEARNING_RATE,
total_step=test_number,
update_freq=UPDATE_FREQ)
agent.restore(save_path)
eval_rewards = []
flag = 0
while flag < test_number:
eval_reward = run_evaluate_episode(test_env, agent)
def main():
# Prepare environments
# env = get_player(
# args.rom, image_size=IMAGE_SIZE, train=True, frame_skip=FRAME_SKIP)
# test_env = get_player(
# args.rom,
# image_size=IMAGE_SIZE,
# frame_skip=FRAME_SKIP,
# context_len=CONTEXT_LEN)
env = gym.make("pseudoslam:RobotExploration-v0")
env = MonitorEnv(env, param={'goal': args.goal, 'obs': args.obs})
# obs = env.reset()
# print(obs.shape)
# raise NotImplementedError
# Init Prioritized Replay Memory
per = ProportionalPER(alpha=0.6, seg_num=args.batch_size, size=MEMORY_SIZE)
suffix = args.suffix + "_Rp{}_Goal{}_Obs{}".format(args.Rp, args.goal,
args.obs)
logdir = os.path.join(args.logdir, suffix)
if not os.path.exists(logdir):
os.mkdir(logdir)
logger.set_dir(logdir)
modeldir = os.path.join(args.modeldir, suffix)
if not os.path.exists(modeldir):
os.mkdir(modeldir)
# Prepare PARL agent
act_dim = env.action_space.n
model = AtariModel(act_dim)
if args.alg == 'ddqn':
algorithm = PrioritizedDoubleDQN(model,
act_dim=act_dim,
gamma=GAMMA,
lr=LEARNING_RATE)
elif args.alg == 'dqn':
algorithm = PrioritizedDQN(model,
act_dim=act_dim,
gamma=GAMMA,
lr=LEARNING_RATE)
agent = AtariAgent(algorithm, act_dim=act_dim, update_freq=UPDATE_FREQ)
if os.path.exists(args.load):
agent.restore(args.load)
# Replay memory warmup
total_step = 0
with tqdm(total=MEMORY_SIZE, desc='[Replay Memory Warm Up]') as pbar:
mem = []
while total_step < MEMORY_WARMUP_SIZE:
total_reward, steps, _, _ = run_episode(env,
agent,
per,
mem=mem,
warmup=True)
total_step += steps
pbar.update(steps)
per.elements.from_list(mem[:int(MEMORY_WARMUP_SIZE)])
# env_name = args.rom.split('/')[-1].split('.')[0]
test_flag = 0
total_steps = 0
pbar = tqdm(total=args.train_total_steps)
save_steps = 0
while total_steps < args.train_total_steps:
# start epoch
total_reward, steps, loss, info = run_episode(env,
agent,
per,
train=True)
total_steps += steps
save_steps += steps
pbar.set_description('[train]exploration:{}'.format(agent.exploration))
summary.add_scalar('train/score', total_reward, total_steps)
summary.add_scalar('train/loss', loss,
total_steps) # mean of total loss
summary.add_scalar('train/exploration', agent.exploration, total_steps)
summary.add_scalar('train/steps', steps, total_steps)
for key in info.keys():
summary.add_scalar('train/' + key, info[key], total_steps)
pbar.update(steps)
if total_steps // args.test_every_steps >= test_flag:
print('start test!')
while total_steps // args.test_every_steps >= test_flag:
test_flag += 1
pbar.write("testing")
test_rewards = []
for _ in tqdm(range(3), desc='eval agent'):
eval_reward = run_evaluate_episode(env, agent)
test_rewards.append(eval_reward)
eval_reward = np.mean(test_rewards)
logger.info(
"eval_agent done, (steps, eval_reward): ({}, {})".format(
total_steps, eval_reward))
summary.add_scalar('eval/reward', eval_reward, total_steps)
if save_steps >= 100000:
modeldir_ = os.path.join(modeldir, 'itr_{}'.format(total_steps))
if not os.path.exists(modeldir_):
os.mkdir(modeldir_)
print('save model!', modeldir_)
agent.save(modeldir_)
save_steps = 0
pbar.close()