Environment Maps: Structured Environmental Representations for Long-Horizon Agents

1. 1Identify the limitations of current large language models (LLMs) in long-horizon settings
2. 2Develop a persistent and agent-agnostic representation of the environment
3. 3Implement Environment Maps to reduce cascading errors and environmental stochasticity
4. 4Evaluate the performance of Environment Maps in various long-horizon tasks

AI engineers and researchers working on long-horizon agents and automation of complex software workflows can benefit from Environment Maps to improve agent performance and reduce errors

💡 Environment Maps provide a structured representation of the environment to improve agent performance in long-horizon settings

💡 Environment Maps help long-horizon agents avoid cascading errors and stochasticity