𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗮𝗯𝗹𝗲 𝗖𝗮𝘂𝘀𝗮𝗹 𝗥𝗟 𝗳𝗼𝗿 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗔𝗻𝗼𝗺𝗮𝗹𝘆 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗲
Standard Reinforcement Learning (RL) acts like a black box. It learns patterns from data but does not understand why things happen.
In satellite operations, this is dangerous. If an agent sees a drop in solar power, it might assume the sun is dimming. If the real cause is space debris, the agent's response could crash the system.
I explored a solution: Explainable Causal Reinforcement Learning (ECRL).
This approach uses Structural Causal Models (SCM). Instead of just seeing correlations, the agent understands cause and effect. It can answer: "What would happen if I took a different action?"
This is vital when working with global teams. Engineers, mission planners, and regulators all need answers. They also speak different languages and have different needs.
My research focused on three pillars:
- Causal Discovery: The agent learns the relationship between variables like thruster temperature and fuel flow.
- Explainability: The agent produces a reasoning path. It shows the "why" behind every decision.
- Multilingual Adaptation: The system translates technical logic into different languages.
I found that translation is not enough. Cultural context matters.
- Japanese stakeholders often prefer summaries that emphasize group consensus.
- German stakeholders often want precise probabilities and data.
- Arabic-speaking officials may require formal, safety-focused justifications.
The ECRL system handles these needs by building a three-level explanation hierarchy:
- Executive Level: Simple summaries for quick decisions.
- Technical Level: Detailed causal paths for engineers.
- Deep Level: Full mathematical proofs for researchers.
By combining causality with multilingual AI, we move from black-box automation to transparent, trustworthy satellite operations.
Optional learning community: https://t.me/GyaanSetuAi