What is DelveRL designed for?

DelveRL is a turn-based roguelike specifically built to train game-playing AI agents. It was created to solve the problem that most existing games are difficult to integrate with agent training systems.

What is included in the open-source release?

The release includes the game itself, training code, a checkpoint, bridge documentation, and raw benchmarks. Everything runs locally, including the batched renderer-free environments and the recurrent PPO trainer.

How well does the baseline agent perform?

The included baseline reaches a median floor of 18, with extended runs reaching floor 33.

Large Language ModelsOpen-Source AIr/MachineLearning2d agoAug 23, 2026, 04:01 JST2 min read

Developer releases open-source roguelike game built for AI agent training

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Key takeaway

A developer released DelveRL, an open-source roguelike game designed to train AI agents.
The game runs locally with a structured API and procedural levels.
The included baseline agent reaches floor 18 on average and floor 33 in extended runs.

3 Key Points

What happened
A developer created DelveRL, an open-source turn-based roguelike designed specifically to train game-playing AI agents. The game features procedural levels, partial observability, and a structured API, running entirely locally with a batched, renderer-free environment and a recurrent PPO trainer included.
Why it matters
Most existing games are difficult to integrate with agent training systems. DelveRL solves this by building a human-playable game from the ground up with agent compatibility in mind, removing barriers that have made projects from DeepMind and OpenAI harder to replicate or extend.
What to watch
The included baseline agent reaches a median floor of 18, with extended runs reaching floor 33. The game, training code, checkpoint, bridge documentation, and raw benchmarks are all open source, inviting the community to develop competing approaches.

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Context & Analysis

The creator was motivated by existing work from DeepMind and OpenAI but identified a practical gap: game environments built for human players typically lack the structured integration points needed to efficiently train agents. DelveRL addresses this by designing the game and its API in tandem from the start, rather than retrofitting an existing title. The design choices—procedural generation for variety, partial observability to create meaningful decisions under uncertainty, and turn-based mechanics for compatibility with batch training—reflect deliberate engineering for agent training at scale. By making the entire stack open source, the developer is inviting the community to explore different training approaches and potentially exceed the baseline performance, which is common practice in published benchmarks.

FAQ

What is DelveRL designed for?: DelveRL is a turn-based roguelike specifically built to train game-playing AI agents. It was created to solve the problem that most existing games are difficult to integrate with agent training systems.
What is included in the open-source release?: The release includes the game itself, training code, a checkpoint, bridge documentation, and raw benchmarks. Everything runs locally, including the batched renderer-free environments and the recurrent PPO trainer.
How well does the baseline agent perform?: The included baseline reaches a median floor of 18, with extended runs reaching floor 33.

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