train.py

import sys
sys.path.append("./common")
sys.path.append("./auto_LiRPA")
from auto_LiRPA import BoundedModule, BoundedTensor
from auto_LiRPA.perturbations import PerturbationLpNorm
from argparser import argparser
from eps_scheduler import EpsilonScheduler
from read_config import load_config
import numpy as np
import cpprb
import re
from attacks import attack
import random
from common.wrappers import make_atari, wrap_deepmind, wrap_pytorch, make_atari_cart
from models import QNetwork, model_setup
import torch.optim as optim
import torch
from torch.nn import CrossEntropyLoss
import torch.autograd as autograd
import math
import time
import os
import argparse
from datetime import datetime
from utils import CudaTensorManager, ActEpsilonScheduler, BufferBetaScheduler, Logger, update_target, get_acrobot_eps, plot 
from my_replay_buffer import ReplayBuffer, NaivePrioritizedBuffer
from common.replay_buffer import PrioritizedReplayBuffer
from async_env import AsyncEnv
from async_rb import AsyncReplayBuffer


USE_CUDA = torch.cuda.is_available()
Variable = lambda *args, **kwargs: autograd.Variable(*args, **kwargs).cuda() if USE_CUDA else autograd.Variable(*args, **kwargs)
UINTS=[np.uint8, np.uint16, np.uint32, np.uint64]


def get_logits_lower_bound(model, state, state_ub, state_lb, eps, C, beta):
    ptb = PerturbationLpNorm(norm=np.inf, eps=eps, x_L=state_lb, x_U=state_ub)
    bnd_state = BoundedTensor(state, ptb)
    pred = model.features(bnd_state, method_opt="forward")
    logits_ilb, _ = model.features.compute_bounds(C=C, IBP=True, method=None)
    if beta < 1e-5:
        logits_lb = logits_ilb
    else:
        logits_clb, _ = model.features.compute_bounds(IBP=False, C=C, method="backward", bound_upper=False)
        logits_lb = beta * logits_clb + (1-beta) * logits_ilb
    return logits_lb


class TimeLogger(object):
    def __init__(self):
        self.time_logs = {}

    def log_time(self, time_id, time):
        if time_id not in self.time_logs:
            self.time_logs[time_id] = 0.0
        self.time_logs[time_id] += time

    def __call__(self, time_id, time):
        self.log_time(time_id, time)

    def clear(self):
        self.time_logs = {}

    def print(self):
        print_str = ""
        for t in self.time_logs:
            print_str += "{}={:.4f} ".format(t, self.time_logs[t])
        print(print_str + "\n")

log_time = TimeLogger()


def logits_margin(logits, y):
    comp_logits = logits - torch.zeros_like(logits).scatter(1, torch.unsqueeze(y, 1), 1e10)
    sec_logits, _ = torch.max(comp_logits, dim=1)
    margin = sec_logits - torch.gather(logits, 1, torch.unsqueeze(y, 1)).squeeze(1)
    margin = margin.sum()
    return margin


def compute_td_loss(current_model, target_model, batch_size, replay_buffer, per, use_cpp_buffer, use_async_rb, optimizer, gamma, memory_mgr, robust, **kwargs):
    t = time.time()
    dtype = kwargs['dtype']
    if per:
        buffer_beta = kwargs['buffer_beta']
        if use_async_rb:
            if not replay_buffer.sample_available():
                replay_buffer.async_sample(batch_size, buffer_beta)
            res = replay_buffer.wait_sample()
            replay_buffer.async_sample(batch_size, buffer_beta)
        else:
            res = replay_buffer.sample(batch_size, buffer_beta)
        if use_cpp_buffer:
            state, action, reward, next_state, done, indices, weights = res['obs'], res['act'], res['rew'], res['next_obs'], res['done'], res['indexes'], res['weights']
        else:
            state, action, reward, next_state, done, weights, indices = res[0], res[1], res[2], res[3], res[4], res[5], res[6]
    else:
        if use_async_rb:
            if replay_buffer.sample_available():
                replay_buffer.async_sample(batch_size)
            res = replay_buffer.wait_sample()
            replay_buffer.async_sample(batch_size)
        else:
            res = replay_buffer.sample(batch_size)
        if use_cpp_buffer:
            state, action, reward, next_state, done = res['obs'], res['act'], res['rew'], res['next_obs'], res['done']
        else:
            state, action, reward, next_state, done = res[0], res[1], res[2], res[3], res[4]
    if use_cpp_buffer and not use_async_rb:
        action = action.transpose()[0].astype(int)
        reward = reward.transpose()[0].astype(int)
        done = done.transpose()[0].astype(int)
    log_time('sample_time', time.time() - t)

    t = time.time()
    numpy_weights = weights
    if per:
        state, next_state, action, reward, done, weights = memory_mgr.get_cuda_tensors(state, next_state, action, reward, done, weights)
    else:
        state, next_state, action, reward, done = memory_mgr.get_cuda_tensors(state, next_state, action, reward, done)

    bound_solver = kwargs.get('bound_solver', 'cov')
    optimizer.zero_grad()

    state = state.to(torch.float)
    next_state = next_state.to(torch.float)
    # Normalize input pixel to 0-1
    if dtype in UINTS:
        state /= 255
        next_state /= 255
        state_max = 1.0
        state_min = 0.0
    else:
        state_max = float('inf')
        state_min = float('-inf')
    beta = kwargs.get('beta', 0)

    if robust and bound_solver != 'pgd':
        cur_q_logits = current_model(state, method_opt="forward")
        tgt_next_q_logits = target_model(next_state, method_opt="forward")
    else:
        cur_q_logits = current_model(state)
        tgt_next_q_logits = target_model(next_state)
    if robust:
        eps = kwargs['eps']
    cur_q_value = cur_q_logits.gather(1, action.unsqueeze(1)).squeeze(1)

    tgt_next_q_value = tgt_next_q_logits.max(1)[0]
    expected_q_value = reward + gamma * tgt_next_q_value * (1 - done)
    '''
    # Merge two states into one batch
    state = state.to(torch.float)
    if dtype in UINTS:
        state /= 255
    state_and_next_state = torch.cat((state, next_state), 0)
    logits = current_model(state_and_next_state)
    cur_q_logits = logits[:state.size(0)]
    cur_next_q_logits = logits[state.size(0):]
    tgt_next_q_value  = tgt_next_q_logits.gather(1, torch.max(cur_next_q_logits, 1)[1].unsqueeze(1)).squeeze(1)
    '''

    if kwargs['natural_loss_fn'] == 'huber':
        loss_fn = torch.nn.SmoothL1Loss(reduction='none')
        loss = loss_fn(cur_q_value, expected_q_value.detach())
    else:
        loss  = (cur_q_value - expected_q_value.detach()).pow(2)
    if per:
        loss = loss * weights
        prios = loss + 1e-5
        weights_norm = np.linalg.norm(numpy_weights)

    batch_cur_q_value = torch.mean(cur_q_value)
    batch_exp_q_value = torch.mean(expected_q_value)
    loss = loss.mean()
    td_loss = loss.clone()

    if robust:
        if eps < np.finfo(np.float32).tiny:
            reg_loss = torch.zeros(state.size(0))
            if USE_CUDA:
                reg_loss = reg_loss.cuda()
            if bound_solver == 'pgd':
                labels = torch.argmax(cur_q_logits, dim=1).clone().detach()
                adv_margin = ori_margin = logits_margin(current_model.forward(state), labels)
                optimizer.zero_grad()
        else:
            if bound_solver != 'pgd':
                sa = kwargs.get('sa', None)
                pred = cur_q_logits
                labels = torch.argmax(pred, dim=1).clone().detach()
                c = torch.eye(current_model.num_actions).type_as(state)[labels].unsqueeze(1) - torch.eye(current_model.num_actions).type_as(state).unsqueeze(0)
                I = (~(labels.data.unsqueeze(1) == torch.arange(current_model.num_actions).type_as(labels.data).unsqueeze(0)))
                c = (c[I].view(state.size(0), current_model.num_actions-1, current_model.num_actions))
                sa_labels = sa[labels]
                lb_s = torch.zeros(state.size(0), current_model.num_actions)
                if USE_CUDA:
                    labels = labels.cuda()
                    c = c.cuda()
                    sa_labels = sa_labels.cuda()
                    lb_s = lb_s.cuda()
                env_id = kwargs.get('env_id','')
                if env_id == 'Acrobot-v1':
                    eps_v = get_acrobot_eps(eps)
                    if USE_CUDA:
                        eps_v = eps_v.cuda()
                else:
                    eps_v = eps
                state_ub = torch.clamp(state + eps_v, max=state_max)
                state_lb = torch.clamp(state - eps_v, min=state_min)

                lb = get_logits_lower_bound(current_model, state, state_ub, state_lb, eps_v, c, beta)

                hinge = kwargs.get('hinge', False)
                if hinge:
                   reg_loss, _ = torch.min(lb, dim=1)
                   hinge_c = kwargs.get('hinge_c', 1)
                   reg_loss = torch.clamp(reg_loss, max=hinge_c)
                   reg_loss = - reg_loss
                else:
                    lb = lb_s.scatter(1, sa_labels, lb)
                    reg_loss = CrossEntropyLoss()(-lb, labels)
            else:
                labels = torch.argmax(cur_q_logits, dim=1).clone().detach()
                hinge_c = kwargs.get('hinge_c', 1)
                adv_state = attack(current_model, state, kwargs['attack_config'], logits_margin)
                optimizer.zero_grad()
                adv_margin = logits_margin(current_model.forward(adv_state), labels)
                ori_margin = logits_margin(current_model.forward(state), labels)
                reg_loss = torch.clamp(adv_margin, min=-hinge_c)

        if per:
            reg_loss = reg_loss * weights
        reg_loss = reg_loss.mean()
        kappa = kwargs['kappa']
        loss += kappa * reg_loss

    loss.backward()

    # Gradient clipping.
    grad_norm = 0.0
    max_norm = kwargs['grad_clip']
    if max_norm > 0:
        parameters = current_model.parameters()
        for p in parameters:
            grad_norm += p.grad.data.norm(2).item() ** 2
        grad_norm = np.sqrt(grad_norm)
        clip_coef = max_norm / (grad_norm + 1e-6)
        if clip_coef < 1:
            for p in parameters:
                p.grad.data.mul_(clip_coef)

    # update weights
    optimizer.step()

    nn_time = time.time() - t
    log_time('nn_time', time.time() - t)
    t = time.time()
    if per:
        replay_buffer.update_priorities(indices, prios.data.cpu().numpy())
    log_time('reweight_time', time.time() - t)

    res = (loss, grad_norm, weights_norm, td_loss, batch_cur_q_value, batch_exp_q_value)
    if robust:
        if bound_solver == 'pgd':
            res += (ori_margin, adv_margin)
        res += (reg_loss,)
    return res


def mini_test(model, config, logger, dtype, num_episodes=10, max_frames_per_episode=30000):
    logger.log('start mini test')
    training_config = config['training_config']
    env_params = training_config['env_params']
    env_params['clip_rewards'] = False
    env_params['episode_life'] = False
    env_id = config['env_id']

    if 'NoFrameskip' not in env_id:
        env = make_atari_cart(env_id)
    else:
        env = make_atari(env_id)
        env = wrap_deepmind(env, **env_params)
        env = wrap_pytorch(env)
    state = env.reset()
    all_rewards = []
    episode_reward = 0

    seed = random.randint(0, sys.maxsize)
    logger.log('reseting env with seed', seed)
    env.seed(seed)
    state = env.reset()

    episode_idx = 1
    this_episode_frame = 1
    for frame_idx in range(1, num_episodes * max_frames_per_episode + 1):
        state_tensor = torch.from_numpy(np.ascontiguousarray(state)).unsqueeze(0).cuda().to(torch.float32)
        if dtype in UINTS:
            state_tensor /= 255
        action = model.act(state_tensor)[0]
        next_state, reward, done, _ = env.step(action)

        # logger.log(action)
        state = next_state
        episode_reward += reward
        if this_episode_frame == max_frames_per_episode:
            logger.log('maximum number of frames reached in this episode, reset environment!')
            done = True

        if done:
            logger.log('reseting env with seed', seed)
            state = env.reset()
            all_rewards.append(episode_reward)
            logger.log('episode {}/{} reward: {:6g}'.format(episode_idx, num_episodes, all_rewards[-1]))
            episode_reward = 0
            this_episode_frame = 1
            episode_idx += 1
            if episode_idx > num_episodes:
                break
        else:
            this_episode_frame += 1
    return np.mean(all_rewards)


def main(args):
    config = load_config(args)
    prefix = config['env_id']
    training_config = config['training_config']
    if config['name_suffix']:
        prefix += config['name_suffix']
    if config['path_prefix']:
       prefix = os.path.join(config['path_prefix'], prefix)
    if not os.path.exists(prefix):
        os.makedirs(prefix)

    train_log = os.path.join(prefix, 'train.log')
    logger = Logger(open(train_log, "w"))
    logger.log('Command line:', " ".join(sys.argv[:]))
    logger.log(args)
    logger.log(config)

    env_params = training_config['env_params']
    env_id = config['env_id']
    if "NoFrameskip" not in env_id:
        env = make_atari_cart(env_id)
    else:
        env = make_atari(env_id)
        env = wrap_deepmind(env, **env_params)
        env = wrap_pytorch(env)

    seed = training_config['seed']
    env.seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    torch.manual_seed(seed)

    state = env.reset()
    dtype = state.dtype
    logger.log("env_shape: {}, num of actions: {}".format(env.observation_space.shape, env.action_space.n))
    if "NoFrameskip" in env_id:
        logger.log('action meaning:', env.unwrapped.get_action_meanings()[:env.action_space.n])

    robust = training_config.get('robust', False)
    adv_train = training_config.get('adv_train', False)
    bound_solver = training_config.get('bound_solver', 'cov')
    attack_config = {}
    if adv_train or bound_solver == 'pgd':
        test_config = config['test_config']
        attack_config = training_config["attack_config"]
        adv_ratio = training_config.get('adv_ratio', 1)
        if adv_train:
            logger.log('using adversarial examples for training, adv ratio:', adv_ratio)
        else:
            logger.log('using pgd regularization training')
    if robust or adv_train:
        schedule_start = training_config['schedule_start']
        schedule_length = training_config['schedule_length']
        starting_epsilon=  training_config['start_epsilon']
        end_epsilon = training_config['epsilon']
        epsilon_scheduler = EpsilonScheduler(training_config.get("schedule_type", "linear"), schedule_start, schedule_start+schedule_length-1, starting_epsilon, end_epsilon, 1)
        max_eps = end_epsilon

    model_width = training_config['model_width']
    robust_model = robust and bound_solver != 'pgd'
    dueling = training_config.get('dueling', True)

    current_model = model_setup(env_id, env, robust_model, logger, USE_CUDA, dueling, model_width)
    target_model = model_setup(env_id, env, robust_model, logger, USE_CUDA, dueling, model_width)

    load_path = training_config["load_model_path"]
    if  load_path != "" and os.path.exists(load_path):
        load_frame = int(re.findall('^.*frame_([0-9]+).pth$',load_path)[0])
        logger.log('\ntrain from model {}, current frame index is {}\n'.format(load_path, load_frame))
        current_model.features.load_state_dict(torch.load(load_path))
        target_model.features.load_state_dict(torch.load(load_path))
    else:
        logger.log('\ntrain from scratch')
        load_frame = 1

    lr = training_config['lr']
    grad_clip = training_config['grad_clip']
    natural_loss_fn = training_config['natural_loss_fn']
    optimizer = optim.Adam(current_model.parameters(), lr=lr, eps=training_config['adam_eps'])
    # Do not evaluate gradient for target model.
    for param in target_model.features.parameters():
        param.requires_grad = False

    buffer_config = training_config['buffer_params']
    replay_initial = buffer_config['replay_initial']
    buffer_capacity = buffer_config['buffer_capacity']
    use_cpp_buffer = training_config["cpprb"]
    use_async_rb = training_config['use_async_rb']
    num_frames = training_config['num_frames']
    batch_size = training_config['batch_size']
    gamma = training_config['gamma']

    if use_cpp_buffer:
        logger.log('using cpp replay buffer')
        if use_async_rb:
            replay_buffer_ctor = AsyncReplayBuffer(initial_state=state, batch_size=batch_size)
        else:
            replay_buffer_ctor = cpprb.PrioritizedReplayBuffer
    else:
        logger.log('using python replay buffer')
    per = training_config['per']

    if per:
        logger.log('using prioritized experience replay.')
        alpha = buffer_config['alpha']
        buffer_beta_start = buffer_config['buffer_beta_start']
        buffer_beta_frames = buffer_config.get('buffer_beta_frames', -1)
        if buffer_beta_frames < replay_initial:
            buffer_beta_frames = num_frames - replay_initial
            logger.log('beffer_beta_frames reset to ', buffer_beta_frames)
        buffer_beta_scheduler = BufferBetaScheduler(buffer_beta_start, buffer_beta_frames, start_frame=replay_initial)
        if use_cpp_buffer:
            replay_buffer = replay_buffer_ctor(size=buffer_capacity,
                    # env_dict={"obs": {"shape": state.shape, "dtype": np.uint8},
                    env_dict={"obs": {"shape": state.shape, "dtype": dtype},
                        "act": {"shape": 1, "dtype": np.uint8},
                        "rew": {},
                        # "next_obs": {"shape": state.shape, "dtype": np.uint8},
                        "next_obs": {"shape": state.shape, "dtype": dtype},
                        "done": {}}, alpha=alpha, eps = 0.0)  # We add eps manually in training loop
        else:
            replay_buffer = PrioritizedReplayBuffer(buffer_capacity, alpha=alpha)

    else:
        logger.log('using regular replay.')
        if use_cpp_buffer:
            replay_buffer =cpprb.ReplayBuffer(buffer_capacity,
                    # {"obs": {"shape": state.shape, "dtype": np.uint8},
                    {"obs": {"shape": state.shape, "dtype": dtype},
                        "act": {"shape": 1, "dtype": np.uint8},
                        "rew": {},
                        # "next_obs": {"shape": state.shape, "dtype": np.uint8},
                        "next_obs": {"shape": state.shape, "dtype": dtype},
                        "done": {}})
        else:
            replay_buffer = ReplayBuffer(buffer_capacity)

    update_target(current_model, target_model)

    act_epsilon_start = training_config['act_epsilon_start']
    act_epsilon_final = training_config['act_epsilon_final']
    act_epsilon_decay = training_config['act_epsilon_decay']
    act_epsilon_method = training_config['act_epsilon_method']
    if training_config.get('act_epsilon_decay_zero', True):
        decay_zero = num_frames
    else:
        decay_zero = None
    act_epsilon_scheduler = ActEpsilonScheduler(act_epsilon_start, act_epsilon_final, act_epsilon_decay, method=act_epsilon_method, start_frame=replay_initial, decay_zero=decay_zero)

    # Use optimized cuda memory management
    memory_mgr = CudaTensorManager(state.shape, batch_size, per, USE_CUDA, dtype=dtype)

    losses = []
    td_losses = []
    batch_cur_q = []
    batch_exp_q = []

    sa = None
    kappa = None
    hinge = False
    if robust:
        logger.log('using convex relaxation certified classification loss as a regularization!')
        kappa = training_config['kappa']
        reg_losses = []
        sa = np.zeros((current_model.num_actions, current_model.num_actions - 1), dtype = np.int32)
        for i in range(sa.shape[0]):
            for j in range(sa.shape[1]):
                if j < i:
                    sa[i][j] = j
                else:
                    sa[i][j] = j + 1
        sa = torch.LongTensor(sa)
        hinge = training_config.get('hinge', False)
        logger.log('using hinge loss (default is cross entropy): ', hinge)


    if training_config['use_async_env']:
        # Create an environment in a separate process, run asychronously
        async_env = AsyncEnv(env_id, result_path=prefix, draw=training_config['show_game'], record=training_config['record_game'], env_params=env_params, seed=seed)

    # initialize parameters in logging
    all_rewards = []
    episode_reward = 0
    act_epsilon = np.nan
    grad_norm = np.nan
    weights_norm = np.nan
    best_test_reward = -float('inf')
    buffer_stored_size = 0
    if adv_train:
        attack_count = 0
        suc_count = 0
    if robust and bound_solver == 'pgd':
        ori_margin, adv_margin = np.nan, np.nan

    start_time = time.time()
    period_start_time = time.time()

    # Main Loop
    for frame_idx in range(load_frame, num_frames + 1):
        # Step 1: get current action
        frame_start = time.time()
        t = time.time()

        eps = 0
        if adv_train or robust:
            eps = epsilon_scheduler.get_eps(frame_idx, 0)

        act_epsilon = act_epsilon_scheduler.get(frame_idx)
        if adv_train and eps != np.nan and eps >= np.finfo(np.float32).tiny:
            ori_state_tensor = torch.from_numpy(np.ascontiguousarray(state)).unsqueeze(0).cuda().to(torch.float32)
            if dtype in UINTS:
                ori_state_tensor /= 255
            attack_config['params']['epsilon'] = eps
            if random.random() < adv_ratio:
                attack_count += 1
                state_tensor = attack(current_model, ori_state_tensor, attack_config)
                if current_model.act(state_tensor)[0] != current_model.act(ori_state_tensor)[0]:
                    suc_count += 1
            else:
                state_tensor = ori_state_tensor
            action = current_model.act(state_tensor, act_epsilon)[0]
        else:
            with torch.no_grad():
                state_tensor = torch.from_numpy(np.ascontiguousarray(state)).unsqueeze(0).cuda().to(torch.float32)
                if dtype in UINTS:
                    state_tensor /= 255
                action = current_model.act(state_tensor, act_epsilon)[0]

        # torch.cuda.synchronize()
        log_time('act_time', time.time() - t)

        # Step 2: run environment
        t = time.time()
        if training_config['use_async_env']:
            async_env.async_step(action)
        else:
            next_state, reward, done, _ = env.step(action)
        log_time('env_time', time.time() - t)


        # Step 3: save to buffer
        # For asynchronous env, defer saving
        if not training_config['use_async_env']:
            t = time.time()
            if use_cpp_buffer:
                replay_buffer.add(obs=state, act=action, rew=reward, next_obs=next_state, done=done)
            else:
                replay_buffer.push(state, action, reward, next_state, done)
            log_time('save_time', time.time() - t)

        if use_cpp_buffer:
            buffer_stored_size = replay_buffer.get_stored_size()
        else:
            buffer_stored_size = len(replay_buffer)

        beta = np.nan
        buffer_beta = np.nan
        t = time.time()

        if buffer_stored_size > replay_initial:
            if training_config['per']:
                buffer_beta = buffer_beta_scheduler.get(frame_idx)
            if robust:
                convex_final_beta = training_config['convex_final_beta']
                convex_start_beta = training_config['convex_start_beta']
                beta = (max_eps - eps * (1.0 - convex_final_beta)) / max_eps * convex_start_beta

            res = compute_td_loss(current_model, target_model, batch_size, replay_buffer, per, use_cpp_buffer, use_async_rb, optimizer, gamma, memory_mgr, robust, buffer_beta=buffer_beta, grad_clip=grad_clip, natural_loss_fn=natural_loss_fn, eps=eps, beta=beta, sa=sa, kappa=kappa, dtype=dtype, hinge=hinge, hinge_c=training_config.get('hinge_c', 1), env_id=env_id, bound_solver=bound_solver, attack_config=attack_config)
            loss, grad_norm, weights_norm, td_loss, batch_cur_q_value, batch_exp_q_value = res[0], res[1], res[2], res[3], res[4], res[5]
            if robust:
                reg_loss = res[-1]
                reg_losses.append(reg_loss.data.item())
                if bound_solver == 'pgd':
                    ori_margin, adv_margin = res[-3].data.item(), res[-2].data.item()

            losses.append(loss.data.item())
            td_losses.append(td_loss.data.item())
            batch_cur_q.append(batch_cur_q_value.data.item())
            batch_exp_q.append(batch_exp_q_value.data.item())

        log_time('loss_time', time.time() - t)

        # Step 2: run environment (async)
        t = time.time()
        if training_config['use_async_env']:
            next_state, reward, done, _ = async_env.wait_step()
        log_time('env_time', time.time() - t)

        # Step 3: save to buffer (async)
        if training_config['use_async_env']:
            t = time.time()
            if use_cpp_buffer:
                replay_buffer.add(obs=state, act=action, rew=reward, next_obs=next_state, done=done)
            else:
                replay_buffer.push(state, action, reward, next_state, done)
            log_time('save_time', time.time() - t)

        # Update states and reward
        t = time.time()
        state = next_state
        episode_reward += reward
        if done:
            if training_config['use_async_env']:
                state = async_env.reset()
            else:
                state = env.reset()
            all_rewards.append(episode_reward)
            episode_reward = 0
        log_time('env_time', time.time() - t)

        # All kinds of result logging
        if frame_idx % training_config['print_frame'] == 0 or frame_idx==num_frames or (robust and abs(frame_idx-schedule_start) < 5) or abs(buffer_stored_size-replay_initial) < 5:
            logger.log('\nframe {}/{}, learning rate: {:.6g}, buffer beta: {:.6g}, action epsilon: {:.6g}'.format(frame_idx, num_frames, lr, buffer_beta, act_epsilon))
            logger.log('total time: {:.2f}, epoch time: {:.4f}, speed: {:.2f} frames/sec, last total loss: {:.6g}, avg total loss: {:.6g}, grad norm: {:.6g}, weights_norm: {:.6g}, latest episode reward: {:.6g}, avg 10 episode reward: {:.6g}'.format(
                time.time() - start_time,
                time.time() - period_start_time,
                training_config['print_frame'] / (time.time() - period_start_time),
                losses[-1] if losses else np.nan,
                np.average(losses[:-training_config['print_frame']-1:-1]) if losses else np.nan,
                grad_norm, weights_norm,
                all_rewards[-1] if all_rewards else np.nan,
                np.average(all_rewards[:-11:-1]) if all_rewards else np.nan))
            logger.log('last td loss: {:.6g}, avg td loss: {:.6g}'.format(
                    td_losses[-1] if td_losses else np.nan,
                    np.average(td_losses[:-training_config['print_frame']-1:-1]) if td_losses else np.nan))
            logger.log('last batch cur q: {:.6g}, avg batch cur q: {:.6g}'.format(
                    batch_cur_q[-1] if batch_cur_q else np.nan,
                    np.average(batch_cur_q[:-training_config['print_frame']-1:-1]) if batch_cur_q else np.nan))
            logger.log('last batch exp q: {:.6g}, avg batch exp q: {:.6g}'.format(
                    batch_exp_q[-1] if batch_exp_q else np.nan,
                    np.average(batch_exp_q[:-training_config['print_frame']-1:-1]) if batch_exp_q else np.nan))
            if robust:
                logger.log('current input epsilon: {:.6g}'.format(eps))
                if bound_solver == 'pgd':
                    logger.log('last logit margin: ori: {:.6g}, adv: {:.6g}'.format(ori_margin, adv_margin))
                else:
                    logger.log('current bound beta: {:.6g}'.format(beta))
                logger.log('last cert reg loss: {:.6g}, avg cert reg loss: {:.6g}'.format(
                    reg_losses[-1] if reg_losses else np.nan,
                    np.average(reg_losses[:-training_config['print_frame']-1:-1]) if reg_losses else np.nan))
                logger.log('current kappa: {:.6g}'.format(kappa))
            if adv_train:
                logger.log('current attack epsilon (same as input epsilon): {:.6g}'.format(eps))
                diff = ori_state_tensor - state_tensor
                diff = np.abs(diff.data.cpu().numpy())
                logger.log('current Linf distortion: {:.6g}'.format(np.max(diff)))
                logger.log('this batch attacked: {}, success: {}, attack success rate: {:.6g}'.format(attack_count, suc_count, suc_count*1.0/attack_count if attack_count>0 else np.nan))
                attack_count = 0
                suc_count = 0
                logger.log('attack stats reseted.')

            period_start_time = time.time()
            log_time.print()
            log_time.clear()

        if frame_idx % training_config['save_frame'] == 0 or frame_idx==num_frames:
            plot(frame_idx, all_rewards, losses, prefix)
            torch.save(current_model.features.state_dict(), '{}/frame_{}.pth'.format(prefix, frame_idx))

        if frame_idx % training_config['update_target_frame'] == 0:
            update_target(current_model, target_model)

        if frame_idx % training_config.get('mini_test', 100000) == 0 and ((robust and beta == 0) or (not robust and frame_idx * 1.0 / num_frames >= 0.8)):
            test_reward = mini_test(current_model, config, logger, dtype)
            logger.log('this test avg reward: {:6g}'.format(test_reward))
            if test_reward >= best_test_reward:
                best_test_reward = test_reward
                logger.log('new best reward {:6g} achieved, update checkpoint'.format(test_reward))
                torch.save(current_model.features.state_dict(), '{}/best_frame_{}.pth'.format(prefix, frame_idx))

        log_time.log_time('total', time.time() - frame_start)


if __name__ == "__main__":
    args = argparser()
    main(args)