DeepAI AI Chat
Log In Sign Up

The NetHack Learning Environment

06/24/2020
by   Heinrich Küttler, et al.
33

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the development of challenging environments that test the limits of current methods. While existing RL environments are either sufficiently complex or based on fast simulation, they are rarely both. Here, we present the NetHack Learning Environment (NLE), a scalable, procedurally generated, stochastic, rich, and challenging environment for RL research based on the popular single-player terminal-based roguelike game, NetHack. We argue that NetHack is sufficiently complex to drive long-term research on problems such as exploration, planning, skill acquisition, and language-conditioned RL, while dramatically reducing the computational resources required to gather a large amount of experience. We compare NLE and its task suite to existing alternatives, and discuss why it is an ideal medium for testing the robustness and systematic generalization of RL agents. We demonstrate empirical success for early stages of the game using a distributed Deep RL baseline and Random Network Distillation exploration, alongside qualitative analysis of various agents trained in the environment. NLE is open source at https://github.com/facebookresearch/nle.

READ FULL TEXT

page 3

page 5

page 14

page 24

page 25

page 26

page 27

09/27/2021

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

The progress in deep reinforcement learning (RL) is heavily driven by th...
03/22/2022

Insights From the NeurIPS 2021 NetHack Challenge

In this report, we summarize the takeaways from the first NeurIPS 2021 N...
10/14/2022

WILD-SCAV: Benchmarking FPS Gaming AI on Unity3D-based Environments

Recent advances in deep reinforcement learning (RL) have demonstrated co...
08/05/2019

DoorGym: A Scalable Door Opening Environment And Baseline Agent

Reinforcement Learning (RL) has brought forth ideas of autonomous robots...
02/19/2023

LapGym – An Open Source Framework for Reinforcement Learning in Robot-Assisted Laparoscopic Surgery

Recent advances in reinforcement learning (RL) have increased the promis...
12/22/2018

Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning

Recent successes in Reinforcement Learning have encouraged a fast-growin...
07/26/2018

ToriLLE: Learning Environment for Hand-to-Hand Combat

We present Toribash Learning Environment (ToriLLE), an interface with vi...

Code Repositories

nle

The NetHack Learning Environment


view repo

Reinforcement-Learning

Reinforcement Learning in the NetHack Environment


view repo

RL_NetHack_2020

Reinforcement Learning solution for NetHack environment


view repo