Papers NetHack
“NetHack” 태그가 달린 논문 28편 · 필터 해제
SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark
Existing work in language grounding typically study single environments. How do we build unified models that apply across multiple environments? We propose the multi-environment Symbolic Interactive Language Grounding be…
Grounded language learningNetHackNovelD: A Simple yet Effective Exploration Criterion
Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. Previous exploration methods (e.g., RND) have achieved strong results in multiple hard tasks. However, if there are multi…
Atari GamesDeep Reinforcement LearningEfficient ExplorationMontezuma's Revenge+1SILG: The Multi-environment Symbolic Interactive Language Grounding Benchmark
Existing work in language grounding typically study single environments. How do we build unified models that apply across multiple environments? We propose the multi-environment Symbolic Interactive Language Grounding be…
Grounded language learningNetHackCORA: Benchmarks, Baselines, and Metrics as a Platform for Continual Reinforcement Learning Agents
Progress in continual reinforcement learning has been limited due to several barriers to entry: missing code, high compute requirements, and a lack of suitable benchmarks. In this work, we present CORA, a platform for Co…
NetHackreinforcement-learningReinforcement LearningReinforcement Learning (RL)MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research
Progress in deep reinforcement learning (RL) is heavily driven by the availability of challenging benchmarks used for training agents. However, benchmarks that are widely adopted by the community are not explicitly desig…
Deep Reinforcement LearningNetHackreinforcement-learningReinforcement Learning (RL)+1BeBold: Exploration Beyond the Boundary of Explored Regions
Efficient exploration under sparse rewards remains a key challenge in deep reinforcement learning. To guide exploration, previous work makes extensive use of intrinsic reward (IR). There are many heuristics for IR, inclu…
Deep Reinforcement LearningEfficient ExplorationNetHackThe NetHack Learning Environment
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 comple…
NetHackNetHack ScoreReinforcement Learning (RL)Systematic GeneralizationExploration in NetHack With Secret Discovery
Roguelike games generally feature exploration problems as a critical, yet often repetitive element of gameplay. Automated approaches, however, face challenges in terms of optimality, as well as due to incomplete informat…
NetHack