NetHack

9 papers with code • 0 benchmarks • 0 datasets

Mean in-game score over 1000 episodes with random seeds not seen during training. See https://arxiv.org/abs/2006.13760 (Section 2.4 Evaluation Protocol) for details.

Libraries

Use these libraries to find NetHack models and implementations

Most implemented papers

The NetHack Learning Environment

facebookresearch/nle NeurIPS 2020

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.

BeBold: Exploration Beyond the Boundary of Explored Regions

facebookresearch/nle 15 Dec 2020

In this paper, we analyze the pros and cons of each method and propose the regulated difference of inverse visitation counts as a simple but effective criterion for IR.

CORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents

agi-labs/continual_rl 19 Oct 2021

In this work, we present CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package.

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

facebookresearch/minihack 27 Sep 2021

By leveraging the full set of entities and environment dynamics from NetHack, one of the richest grid-based video games, MiniHack allows designing custom RL testbeds that are fast and convenient to use.

SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark

vzhong/silg 20 Oct 2021

We hope SILG enables the community to quickly identify new methodologies for language grounding that generalize to a diverse set of environments and their associated challenges.

NovelD: A Simple yet Effective Exploration Criterion

tianjunz/NovelD NeurIPS 2021

We analyze NovelD thoroughly in MiniGrid and found that empirically it helps the agent explore the environment more uniformly with a focus on exploring beyond the boundary.

Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning

ucl-dark/skillhack 23 Jul 2022

In this paper, we investigate how skills can be incorporated into the training of reinforcement learning (RL) agents in complex environments with large state-action spaces and sparse rewards.

Improving Policy Learning via Language Dynamics Distillation

vzhong/language-dynamics-distillation 30 Sep 2022

Recent work has shown that augmenting environments with language descriptions improves policy learning.

Dungeons and Data: A Large-Scale NetHack Dataset

facebookresearch/nle 1 Nov 2022

Recent breakthroughs in the development of agents to solve challenging sequential decision making problems such as Go, StarCraft, or DOTA, have relied on both simulated environments and large-scale datasets.