griver

import gym
import tensorflow as tf
import numpy as np
import itertools
import tensorflow.contrib.layers as layers
from tqdm import trange
from gym.spaces import Discrete, Box

def get_traj(agent, env, max_episode_steps, render, deterministic_acts=False):
  '''

griver

tmux and screen commands

---

Action	tmux	screen
start a new session	tmux tmux new tmux new-session	screen
start a new session with a name	tmux new -s name	screen -S name
re-attach a detached session	tmux attach tmux attach-session	screen -r
re-attach a detached session with a name	tmux attach -t name tmux a -t name	screen -r name
re-attach an attached session (detaching it from elsewhere)	tmux attach -dtmux attach-session -d	screen -dr

griver

@classmethod
def update_from_checkpoint(Cls, save_folder, network, optimizer=None,
                           use_best=False, use_cpu=False, ignore_layers=tuple()):
    """
    Update network and optimizer(if specified) from the checkpoints in the save folder.
    If use_best is True then the data is loaded from the Cls.CHECKPOINT_BEST file
    otherwise the data is loaded from the Cls.CHECKPOINT_LAST file
    Returns the number of global steps past for the loaded checkpoint.
    Arguments:
        save_folder (str): a path to a folder containing summaries and weights