NTRL: Encounter Generation via Reinforcement Learning for Dynamic Difficulty Adjustment in Dungeons and Dragons

TL;DR

This paper introduces NTRL, a reinforcement learning-based system that automates dynamic difficulty adjustment in Dungeons & Dragons, improving combat engagement, strategic depth, and balancing challenge levels compared to traditional methods.

Contribution

NTRL is a novel reinforcement learning approach that automates encounter generation for D&D, optimizing difficulty and engagement through real-time adaptation.

Findings

NTRL extends combat longevity by over 200%.

It increases damage to party members and raises the number of player deaths.

NTRL maintains high win rates (~70%) and outperforms human-designed encounters.

Abstract

Balancing combat encounters in Dungeons & Dragons (D&D) is a complex task that requires Dungeon Masters (DM) to manually assess party strength, enemy composition, and dynamic player interactions while avoiding interruption of the narrative flow. In this paper, we propose Encounter Generation via Reinforcement Learning (NTRL), a novel approach that automates Dynamic Difficulty Adjustment (DDA) in D&D via combat encounter design. By framing the problem as a contextual bandit, NTRL generates encounters based on real-time party members attributes. In comparison with classic DM heuristics, NTRL iteratively optimizes encounters to extend combat longevity (+200%), increases damage dealt to party members, reducing post-combat hit points (-16.67%), and raises the number of player deaths while maintaining low total party kills (TPK). The intensification of combat forces players to act wisely and engage in tactical maneuvers, even though the generated encounters guarantee high win rates (70%). Even in comparison with encounters designed by human Dungeon Masters, NTRL demonstrates superior performance by enhancing the strategic depth of combat while increasing difficulty in a manner that preserves overall game fairness.