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Reinforcement Learning Beyond the Surface: Integrating Ethical Constraints in Autonomous AI Systems

  • Unique Paper ID: 174440
  • Volume: 11
  • Issue: 10
  • PageNo: 3751-3759
  • Abstract:
  • This has made the integration of ethical constraints in reinforcement learning a pressing need in the existing development of autonomous artificial intelligent systems. This paper of research investigates methods in including ethical considerations in the decision-making processes of AI by using four advanced algorithms of RL: In the same vein, we have Constrained Policy Optimization, Reward Shaping with Ethical Penalties, Multi-Agent Ethical Training, and Value-Based Ethical Prioritization. To test these algorithms and give stress on their ability to meet two primary goals, maximizing performance without compromising ethically questionable actions, a simulated multi-agent environment was created. The discovery was made that CPO was successful in achieving an ethical compliance of 92.3% on average at the same time as attaining a system efficiency of 89.5 % which was much higher compared to the basic reinforcement learning algorithms at about 15 % above. Moreover, if the generic approach, Reward Shaping with Ethical Penalties is implemented, the compliance rate is 90.7%, whereas the impact on efficiency was minimal, equal to 87.6%. These suggested strategies are compared with related research studies, which gain up to 18% of higher ethical compliance and 12% more efficiency. Outcomes of these studies show that ethics in AI is worthwhile for integration, as it has been assured that it will someday contribute to the development of systems for dealing with real world challenges responsibly. Thus, this work paves the way for future research to investigate other tangible, and viable, ethical RL methods in other fields.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 10
  • PageNo: 3751-3759

Reinforcement Learning Beyond the Surface: Integrating Ethical Constraints in Autonomous AI Systems

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