DelveRL Makes Roguelikes Agent-Ready
π‘A reproducible open-source roguelike for testing agents on planning, memory, exploration, and risk.
β‘ 30-Second TL;DR
What Changed
DelveRL was designed from the ground up for easy integration with agent harnesses.
Why It Matters
DelveRL lowers the engineering barrier for researchers who want to test reinforcement-learning agents in a strategically rich environment. Its deterministic and renderer-free design should also make experiments easier to reproduce and scale.
What To Do Next
Clone DelveRL, reproduce the recurrent PPO baseline, and then benchmark a policy or memory change against the reported median floor of 18.
Key Points
- β’DelveRL was designed from the ground up for easy integration with agent harnesses.
- β’The environment supports deterministic simulation, procedural levels, partial observability, and batched renderer-free execution.
- β’The open-source release includes the game, training code, checkpoint, bridge documentation, and raw benchmarks.
- β’The included recurrent PPO baseline reaches a median floor of 18, with extended runs reaching floor 33.
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Original source: Reddit r/MachineLearning β
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