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Continuous control tasks often involve high-dimensional, dynamic, and non-linear environments.
Neural Logic Reinforcement Learning
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A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
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When a computer program keeps you in jail
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Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
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How bad is Sacramento’s air, exactly? Google results appear at odds with reality, some say
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Understanding misuse of partially automated vehicles–A discussion of NTSB’s findings of the 2018 mountain view Tesla crash
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Bastani, O.; Pu, Y.; and Solar-Lezama, A. 2019 · 2019
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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Verma, A.; Murali, V.; Singh, R.; Kohli, P.; and Chaudhuri, S. 2019 · 2019
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Interpretable and Explainable Logical Policies via Neurally Guided Symbolic Abstraction
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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
Arrieta, A. B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R.; et al. 2020 · 2020
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A survey of safety and trustworthiness of deep neural networks: Verification, testing, adversarial attack and defence, and interpretability
Huang, X.; Kroening, D.; Ruan, W.; Sharp, J.; Sun, Y.; Thamo, E.; Wu, M.; and Yi, X. 2020 · 2020
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Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement Learning
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A co-evolutionary approach to interpretable reinforcement learning in environments with continuous action spaces
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Neuro-Symbolic Reinforcement Learning with First-Order Logic
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Interpretable Reinforcement Learning for Robotics and Continuous Control
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Naturally Interpretable Control Policies via Graph-Based Genetic Programming
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