Explainable Reinforcement Learning for Autonomous Robotic Decision Making

  • Authors

    • N. Seshagiri Information Technology Pioneer, National Informatics Centre, India Author

    DOI:

    https://doi.org/10.67228/30715725/IJIARE-2025PII1X9P

    Published 08-04-2025

  • Explainable Artificial Intelligence (XAI), Explainable Reinforcement Learning (XRL), Autonomous Robotics, Deep Reinforcement Learning, Decision Making, Human–Robot Interaction, Policy Learning, Intelligent Control, Trustworthy AI, Autonomous Navigation

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    N. Seshagiri, “Explainable Reinforcement Learning for Autonomous Robotic Decision Making”, IJIARE, vol. 8, no. 2, pp. 01–15, Aug. 2025, doi: 10.67228/30715725/IJIARE-2025PII1X9P.

  • Abstract

    Autonomous robotic systems are increasingly deployed in industrial automation, healthcare, logistics, agriculture, defense, and intelligent transportation, where they must make complex decisions in dynamic environments. Reinforcement Learning (RL) enables robots to learn optimal actions through interaction with their environment, but most deep RL models function as black boxes, limiting transparency and trust in safety-critical applications. This paper proposes an Explainable Reinforcement Learning for Autonomous Robotic Decision Making (XORL) framework that integrates reinforcement learning with Explainable AI (XAI) to improve decision interpretability. The framework combines multimodal sensor data, policy optimization, confidence estimation, reward decomposition, policy visualization, and decision traceability to generate understandable explanations for robotic actions. It evaluates performance using metrics such as navigation success, obstacle avoidance, learning stability, computational efficiency, explanation consistency, and reliability. Experimental results demonstrate that XORL enhances decision transparency, operator trust, safety awareness, and autonomous task performance while maintaining competitive learning efficiency, supporting the development of trustworthy and human-centric autonomous robotic systems.

  • References

    [1] R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction, 2nd ed. Cambridge, MA, USA: MIT Press, 2018.

    [2] V. Mnih et al., "Human-level control through deep reinforcement learning," Nature, vol. 518, no. 7540, pp. 529–533, Feb. 2015.

    [3] H. V. Hasselt, A. Guez, and D. Silver, "Deep Reinforcement Learning with Double Q-Learning," in Proc. AAAI Conf. Artificial Intelligence, Phoenix, AZ, USA, 2016, pp. 2094–2100.

    [4] Z. Wang et al., "Dueling Network Architectures for Deep Reinforcement Learning," in Proc. 33rd Int. Conf. Machine Learning (ICML), New York, NY, USA, 2016, pp. 1995–2003.

    [5] J. Schulman et al., "Proximal Policy Optimization Algorithms," arXiv preprint arXiv:1707.06347, 2017.

    [6] T. P. Lillicrap et al., "Continuous Control with Deep Reinforcement Learning," in Proc. Int. Conf. Learning Representations (ICLR), San Juan, Puerto Rico, 2016.

    [7] S. Fujimoto, H. Van Hoof, and D. Meger, "Addressing Function Approximation Error in Actor-Critic Methods," in Proc. 35th Int. Conf. Machine Learning (ICML), Stockholm, Sweden, 2018, pp. 1587–1596.

    [8] T. Haarnoja, A. Zhou, P. Abbeel, and S. Levine, "Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor," in Proc. 35th Int. Conf. Machine Learning (ICML), Stockholm, Sweden, 2018, pp. 1861–1870.

    [9] A. Adadi and M. Berrada, "Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)," IEEE Access, vol. 6, pp. 52138–52160, 2018.

    [10] W. Samek, T. Wiegand, and K.-R. Müller, "Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models," IEEE Signal Processing Magazine, vol. 38, no. 6, pp. 56–68, Nov. 2021.

    [11] D. Gunning and D. Aha, "DARPA's Explainable Artificial Intelligence (XAI) Program," AI Magazine, vol. 40, no. 2, pp. 44–58, Summer 2019.

    [12] S. Milani, N. Topin, M. Veloso, and F. Fang, "A Survey of Explainable Reinforcement Learning," ACM Computing Surveys, vol. 56, no. 7, pp. 1–38, 2024.

    [13] J. Jin, B. Kim, M. T. J. Spaan, and M. A. Wiering, "Explainable Reinforcement Learning: A Survey," IEEE Transactions on Artificial Intelligence, vol. 5, no. 2, pp. 540–559, 2024.

    [14] F. Chen, X. Wu, Y. Ge, and H. Zhang, "Explainable Deep Reinforcement Learning for Safe Autonomous Robotic Decision Making," IEEE Access, vol. 12, pp. 35214–35229, 2024.

    [15] Y. Li, Z. Wang, H. Liu, and J. Chen, "Explainable Reinforcement Learning for Human-Centered Autonomous Robotic Systems," IEEE Transactions on Automation Science and Engineering, vol. 22, no. 1, pp. 115–128, Jan. 2025.

    [16] Gajula, S. (2024). Cybersecurity risk prediction using graph neural networks. Journal of Information Systems Engineering and Management.

    [17] Gajula, S. (2024). Adaptive zero trust architecture for securing financial microservices. Computer Fraud & Security, 2024(12), 643–655. https://doi.org/10.52710/cfs.845

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