Advanced Automation for Comprehensible Causal Explanations of Reinforcement Learning Agents
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This thesis introduces Auto-BENEDICT, a novel, fully automated methodology designed to generate human-comprehensible causal explanations for model-free Reinforcement Learning (RL) agents. The system addresses the trade-off between high performance and transparency in RL by integrating Bayesian Networks for causal inference and Recurrent Neural Networks to forecast future states and actions. The me...
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This thesis introduces Auto-BENEDICT, a novel, fully automated methodology designed to generate human-comprehensible causal explanations for model-free Reinforcement Learning (RL) agents. The system addresses the trade-off between high performance and transparency in RL by integrating Bayesian Networks for causal inference and Recurrent Neural Networks to forecast future states and actions. The me...
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