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September 10, 2025International Journal Of Recent Trends In Multidisciplinary Research

Reinforcement Learning for Autonomous Systems: A Simulation-Based Study

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Authors

DKDeepali Y. KirangeYCYogesh Chaudhari

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Overview

Simulation-based study evaluates RL algorithms like PPO and DDPG for effective driving tasks, suggesting significant potential for future development.

Key Points

  • RL methods demonstrate effective learning of driving behaviors over time, improving performance metrics.
  • Success rate and collision metrics provide quantitative evidence for the efficacy of RL algorithms in autonomous systems.
  • Simulation-based training highlights the potential of RL in achieving better outcomes compared to traditional controllers.
  • Sensitivity to environment changes indicates the need for robust models in real-world applications of RL.

Cite This Study

Kirange et al. (2025) studied this question.

synapsesocial.com/papers/68c1abf154b1d3bfb60e3eeehttps://doi.org/10.59256/ijrtmr.20250504004
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