pkuderov

public async Task<ActionResult> UpdateConfiguration(string login, string password)
{
	if (String.IsNullOrWhiteSpace(login) || String.IsNullOrWhiteSpace(password))
		return RedirectToAction("Index");

	var currentUserId = CurrentUserId;
	var siteAccount = await SharedDbContext.SiteAccounts
		.FirstOrDefaultAsync(sa => sa.OwnerId == currentUserId && sa.AccountType == SiteAccountType.Responses);

	if (siteAccount == null)

pkuderov

using System;
using System.Collections.Generic;
using System.IO;
using System.Linq;
using System.Text;

namespace CharReplacement
{
    internal class Program
    {

pkuderov

// Type: StructPerformance.Program 
// Assembly: StructPerformance, Version=1.0.0.0, Culture=neutral, PublicKeyToken=null
// MVID: 6028B8CC-7F4D-43EF-A8CF-6680A3E9F2CC
// Location: C:\Users\PKuderov\Documents\Projects\Tests\MicroTests\StructPerformance\bin\Release\StructPerformance.exe
// Sequence point data from C:\Users\PKuderov\Documents\Projects\Tests\MicroTests\StructPerformance\bin\Release\StructPerformance.pdb

.class private auto ansi beforefieldinit 
  StructPerformance.Program
    extends [mscorlib]System.Object
{

pkuderov

using System;

namespace Calculator
{
    internal interface IMathProvider<T>
    {
        T Divide(T a, T b);
        T Multiply(T a, T b);
        T Add(T a, T b);
        T Subtract(T a, T b);

pkuderov

using System;

namespace BoringVector
{
    /*
        Здесь тебе нужно написать класс с методами-расширениями структуры Vector:
            - IsZero: проверяет, является ли вектор нулевым, т.е. его координаты близки к нулю (в эпсилон окрестности). За эпсилон здесь и далее берем 1e-6.
            - Normalize: нормализует вектор
            - GetAngleBetween: возвращает угол между двумя векторами в радианах. Примечание: нулевой вектор сонаправлен любому другому.

pkuderov

using System;
using System.Collections.Generic;
using System.Linq;

namespace DrunkFibonacci
{
    internal static class DrunkFibonacci
    {
        /// <summary>

pkuderov

pkuderov

Hierarchical RL sample projects:

• (2-3 ppl) Implement paper Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitives.
• (1-2 ppl) Add Hindsight experience replay to Data-Efficient Hierarchical Reinforcement Learning (HIRO) implementation. Compare with simple HIRO.
  • (2-3 ppl) Compare with Hierarchical Actor-Critic (HAC)
• (1-2 ppl) Add intrinsic motivation to FuN. How it affects performance on sparse reward problems like Montezuma's Revenge?
• (2-3 ppl) Based on this paper, add multi-step reward learning and temporally-extended exploration to a non-HRL agent. Compare its performance with HRL methods (e.g. FuN on Atari, HIRO on continuous actions envs or even MLSH against transferable learning properties).
• (1 ppl) Reimplement paper [Meta Learning Shared Hierarchies](https://arxiv.org/abs/1710.0976