NetHack
22 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.
Benchmarks
These leaderboards are used to track progress in NetHack
Libraries
Use these libraries to find NetHack models and implementations
Most implemented papers
The NetHack Learning Environment
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
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
In this work, we present CORA, a platform for Continual Reinforcement Learning Agents that provides benchmarks, baselines, and metrics in a single code package.
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning
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.
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
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
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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
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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.
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.
Improving Policy Learning via Language Dynamics Distillation
vzhong/language-dynamics-distillation
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Recent work has shown that augmenting environments with language descriptions improves policy learning.
Dungeons and Data: A Large-Scale NetHack Dataset
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.
