Khoi lmBored

Reinforcement Learning: What's Next?

Opening Thesis

The most important claim of this talk is simple: reinforcement learning (RL) is no longer best understood as a narrow subfield about agents playing games. That picture was historically useful, but in 2026 it is too small. RL increasingly functions as a general mechanism for turning prediction into decision-making, for turning static models into agents, and for turning raw capability into behavior that can be optimized against goals, feedback, and constraints. The question "what's next for RL?" therefore should not be framed as "what is the next Atari or AlphaGo moment?" It should be framed as: wherever AI systems must act, search, plan, improve from feedback, or optimize behavior under delayed consequences, RL is trying to re-enter the stack.

That is part of why the field feels different now. In March 2025, ACM announced that Andrew Barto and Richard Sutton had received the 2024 A.M. Turing Award for "developing the conceptual and algorithmic fou

Last Updated: February 18, 2026

Topic: Security Features

CAPTCHA

CAPTCHA is a security mechanism designed to distinguish between human users and bots. While effective at stopping bad actors, CAPTCHA rules can interrupt the user experience and reduce user retention.

To balance security with conversion rate, our service does not present a CAPTCHA on every interaction. Instead, we use conditional trigger logic - a CAPTCHA is displayed only when a request matches specific automated risk rules or when explicitly enabled by an administrator.

	#!/usr/bin/env python3
	"""ANS Exam Downloader - Downloads exam questions and answers from ANS for offline viewing."""

	import contextlib
	import html
	import os
	import re
	import sys
	from pathlib import Path
	from urllib.parse import parse_qs, quote, unquote, urljoin, urlparse

	@ Binary.s
	@ Adam Watkins, Richard Verhoeven
	@ December 2021

	@ Wire the Gertboard as follows:
	@ On J2 On J3
	@ GP0 -> B12
	@ GP1 -> B11
	@ GP4 -> B10
	@ GP7 -> B9

	#!/bin/bash
	#SBATCH --job-name=comrad
	#SBATCH --partition=mcs.gpu.q
	#SBATCH --gres=gpu:1
	#SBATCH --cpus-per-task=16
	#SBATCH --mem=32G
	#SBATCH --time=6:12:00
	#SBATCH --output=/home/20231193/ViZDoom/train_dir/armory_siege_QMIX_20260302_081241_j%j/slurm_%j.out
	#SBATCH --error=/home/20231193/ViZDoom/train_dir/armory_siege_QMIX_20260302_081241_j%j/slurm_%j.err
	#SBATCH --mail-type=BEGIN,END,FAIL

	#include <bits/stdc++.h>
	using namespace std;

	const int MAXN = 100005;
	const double INF = 1e18;

	vector<pair<int, double>> adj[MAXN];
	double dis[MAXN];
	int n, m;

	#!/bin/bash
	set -e

	module purge
	module load cuda11.8/toolkit/11.8.0

	wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda.sh
	bash ~/miniconda.sh -b -p $HOME/miniconda3
	rm ~/miniconda.sh

	#version 330

	//uniform sampler2D cubemap[1];
	uniform sampler2D cubemap0;
	uniform sampler2D cubemap1;

	in vec3 R; // vector from eye to object reflected in the normal direction
	in vec2 outTexCoord;
	out vec4 FragColor;

	<section id="c310623" class="contentSliderBlock contentSliderBlock--white contentSliderBlock--contentSlider contentSliderBlock-310623 u-sectionPadding u-background--white" tabindex="-1" data-uid="310623" data-slider-type="contentSlider" data-number-of-slides="1">
	<div class="row contentSliderBlock-headerRow ">
	<div class="small-24 large-14 contentSliderBlock-headerColumn columns">
	<div class="contentSliderBlock-intro">
	<h3 class="contentSliderBlock-title u-background-text heading--section heading--lineHeightCorrectionSection"><font style="vertical-align: inherit;"><font style="vertical-align: inherit;">

	IEEE Student Branch Eindhoven (SBE)

	</font></font></h3>
	</div>