NetHack
16 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
Latest papers with no code
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
We evaluate our method in the classic videogame NetHack and the text environment ScienceWorld to demonstrate SSO's ability to optimize a set of skills and perform in-context policy improvement.
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
Fine-tuning is a widespread technique that allows practitioners to transfer pre-trained capabilities, as recently showcased by the successful applications of foundation models.
Selective Perception: Optimizing State Descriptions with Reinforcement Learning for Language Model Actors
Large language models (LLMs) are being applied as actors for sequential decision making tasks in domains such as robotics and games, utilizing their general world knowledge and planning abilities.
Scaling Laws for Imitation Learning in Single-Agent Games
Inspired by recent work in Natural Language Processing (NLP) where "scaling up" has resulted in increasingly more capable LLMs, we investigate whether carefully scaling up model and data size can bring similar improvements in the imitation learning setting for single-agent games.
Accelerating exploration and representation learning with offline pre-training
In this work, we follow the hypothesis that exploration and representation learning can be improved by separately learning two different models from a single offline dataset.
SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark
We hope SILG enables the community to quickly identify new methodolo- gies for language grounding that generalize to a diverse set of environments and their associated challenges.
Exploration in NetHack With Secret Discovery
Our algorithm is based on the concept of occupancy maps popular in robotics, adapted to encourage efficient discovery of secret access points.
