mlsanityData to 14 Oct 2026

DSLE: A Learning Environment for Dark Souls Boss Encounters

arXiv 2608.09902 ↗First posted 10 Aug 2026cs.AI cs.NEcited by 0

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Abstract (arXiv)

We introduce the Dark Souls Learning Environment (DSLE), a containerized platform that presents all 22 boss encounters of Dark Souls: Remastered as game-playing agent benchmarks through a Gymnasium-style interface. DSLE combines real-time combat, high-dimensional visual input, and sparse terminal rewards, with each environment step being a real action executed against the running game. To support controlled comparison, we define DSLE-5, a representative five-boss subset, spanning a melee fight, a spatially constrained arena, an environmental-hazard fight, a multi-target fight, and a fast final-boss fight, that we recommend as the starting suite for agents built on DSLE. On DSLE-5 we evaluate a random policy, an expert system, an evolutionary baseline, and PPO and DQN agents trained from visual input. The expert system and the evolutionary baseline each defeat the Asylum Demon, the game's tutorial boss (63% and 43% peak win rates), but none of the five methods defeats the other four DSLE-5 bosses; PPO and DQN show no measurable learning (at most 0.33% win rate on the tutorial boss, 0% elsewhere) within a budget that already costs tens of wall-clock hours per run. A broader study running the evolutionary baseline across all 22 encounters under advantaged all level-50 stats yields wins on only a handful of additional early-game bosses and leaves the rest unwon. The failure cases range from sub-10-second deaths in cramped, multi-target encounters to minute-long stalemates that inflict almost no damage, and we report them through survival time and damage dealt rather than win rate alone.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 24 references that are in the map. First posted on arXiv 10 Aug 2026.

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15 of 24 references.

References · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
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  10. 2017Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learningcited by 150
  11. 2024Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learningcited by 88
  12. 2021Counter-Strike Deathmatch with Large-Scale Behavioural Cloningcited by 50
3 more
  1. 2024Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Modelscited by 33
  2. 2025GVGAI-LLM: Evaluating Large Language Model Agents with Infinite Gamescited by 9
  3. 2025Playing Atari Space Invaders with Sparse Cosine Optimized Policy Evolutioncited by 1

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