Large Language Model Reinforcement Learning Explainer based on Aristotelian Practical Syllogism

Interactive system for explaining Reinforcement Learning agent decisions applied to urban planning, with automatic adaptation of technical vocabulary to the audience. DescriptionThis web portal allows you to explain the decisions of an urban RL agent at three different technical levels: Level 1 - Common Language: For the general public without technical knowledgeLevel 2 - Professional: For architects and urban planners with specialized terminologyLevel 3 - RL Technical: For data scientists and RL/ML researchersMain FeaturesTechnical Level SystemAutomatic vocabulary adaptation based on the audienceThree levels of complexity with specialized promptsComparison mode to view all three responses simultaneously Intelligent Cache SystemMD5-based cache to prevent duplicate queriesInstant responses for repeated queriesReduced costs and response time Metrics and AnalyticsResponse generation timeToken count (input/output)Cache usage indicatorComplete conversation history Flexible ConfigurationEnvironment variables configurable from the interfaceAdvanced system customization PromptPredefined presets (simple and technical)Automatic alerts for missing configurations History and TraceabilityFull log of all queriesTimestamps and metrics per conversationAbility to clear history and cache