Exploration in NetHack With Secret Discovery
원문 초록
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 information, such as from the presence of secret doors. This paper presents an algorithmic approach to exploration of roguelike dungeon environments. Our design aims to minimize exploration time, balancing coverage and discovery of secret areas with resource cost. Our algorithm is based on the concept of occupancy maps popular in robotics, adapted to encourage efficient discovery of secret access points. Through extensive experimentation on NetHack maps we show that this technique is significantly more efficient than simpler greedy approaches and an existing automated player. We further investigate optimized parameterization for the algorithm through a comprehensive data analysis. These results point towards better automation for players as well as heuristics applicable to fully automated gameplay.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Syllabus: Portable Curricula for Reinforcement Learning Agents
Online Intrinsic Rewards for Decision Making Agents from Large Language Model Feedback
Playing NetHack with LLMs: Potential & Limitations as Zero-Shot Agents
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
Fine-tuning Reinforcement Learning Models is Secretly a Forgetting Mitigation Problem
Skill Set Optimization: Reinforcing Language Model Behavior via Transferable Skills
토론
이 논문과 연결된 포럼 글이 아직 없습니다.