alex-petrenko

import sys
import math
import random
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt


def encoder(observation):
    with tf.variable_scope('encoder', reuse=tf.AUTO_REUSE):

alex-petrenko

import os
import shutil
import time
from os.path import join

import cv2
import deepmind_lab
import gym
import numpy as np
from gym.utils import seeding

alex-petrenko

class VizdoomEnvMultiplayer(VizdoomEnv):
    def __init__(self, level, player_id, num_players, skip_frames, level_map='map01'):
        super().__init__(level, skip_frames=skip_frames, level_map=level_map)

        self.player_id = player_id
        self.num_players = num_players
        self.timestep = 0
        self.update_state = True

    def _is_server(self):

alex-petrenko

#include <Corrade/Containers/GrowableArray.h>
#include <Corrade/Containers/Optional.h>

#include <Magnum/GL/Buffer.h>
#include <Magnum/GL/DefaultFramebuffer.h>
#include <Magnum/GL/Mesh.h>
#include <Magnum/GL/Renderer.h>
#include <Magnum/Math/Color.h>
#include <Magnum/Math/Matrix4.h>
#include <Magnum/MeshTools/Interleave.h>

alex-petrenko

from typing import Tuple

import torch
import torch.nn as nn
from torch.nn.utils.rnn import PackedSequence, invert_permutation


def _build_pack_info_from_dones(
    dones, T: int
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:

alex-petrenko

"""
This script clones students' github repos with HW solutions, checks out a commit specified by the student,
and runs corresponding tests, generating a directory with reports.

Just place the script into the root of cloned "tests" repo and change global variables below appropriately.
Then run as "python batch_grade.py"

"""

alex-petrenko

import os
import sys
import argparse
import subprocess

from os import listdir
from os.path import isfile, join


def main():