TextWorld
https://github.com/microsoft/TextWorld
Microsoft Research's TextWorld — a sandbox for training and evaluating RL agents on text-based games. Procedurally generates interactive-fiction games of tunable difficulty (map size, quest length, ingredients) with full ground-truth knowledge graphs. Used as the basis for the FirstTextWorld and TextWorld Challenge competitions.
At a glance
- Type
- Text-adventure RL benchmark — procedurally-generated IF games
- Tier
- T3
- Section
- Memory benchmarks & evaluation
- Created
- 2018-06 (Côté et al. arXiv 1806.11532)
- Latest release
- not applicable — not OSS
- License
- MIT
- GitHub
- 1.3k★ Python
- Pricing
- not applicable — open benchmark; free to use
- Funding
- not applicable — not commercial
Taxonomy
- storage
- n/a
- retrieval
- n/a
- persistence
- n/a
- update
- read-only
- unit
- n/a
- governance
- n/a
- conflict
- n/a
When to use
Optimised for: not applicable - eval dataset, not a system
Anti-fit: not applicable - eval dataset, not a system
Pros & cons
Pros
Procedural generation gives unlimited variants; ground-truth KGs enable rigorous memory-architecture evaluation; lightweight (text-only).
Cons
Pure text limits transfer to embodied agents; quest structures simpler than human-authored IF; less popular post-LLM-era (LLMs nearly solve handcrafted IF zero-shot).
Claims & capabilities
Microsoft Research; ICML 2018; ground-truth KG for every generated game enables ablation studies on memory architectures
Technical surface
- API surface
- not applicable — eval dataset, not a system
- Backend storage
- not applicable — eval dataset, not a system
- Deployment
- not applicable — run locally
- Embedding model
- not applicable — eval dataset, not a system
- Multi-tenancy
- not applicable — eval dataset, not a system
- MCP
- not applicable — benchmark / evaluation harness
- A2A
- not applicable — benchmark / evaluation harness
- OpenTelemetry
- not applicable — benchmark / evaluation harness
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