SafePath-RL: Risk-Constrained Reinforcement Learning with Deliberative Reasoning for Autonomous Decision Agents
Keywords:
Risk-constrained reinforcement learning, deliberative reasoning, autonomous agents, safety-critical systems, socio-technical governance, system architecture, fairness, sustainabilityAbstract
The deployment of autonomous decision agents in safety-critical domains such as autonomous driving, healthcare, and industrial automation demands a rigorous framework that reconciles the efficiency of reinforcement learning with the normative constraints of risk management. This paper introduces SafePath-RL, a hybrid architecture that integrates risk-constrained reinforcement learning with deliberative reasoning mechanisms drawn from cognitive science and formal verification. Rather than treating safety as a post hoc patch, SafePath-RL embeds risk budgets into the learning objective and uses a two-system reasoning pipeline—combining fast reactive policies with slower, deliberative planning—to reduce the probability of catastrophic failures. The paper examines the system-level trade-offs among safety, performance, and computational overhead, and discusses the architectural choices that enable real-time operation without sacrificing formal guarantees. We also analyze the governance implications, including regulatory compliance, fairness across deployment contexts, and the sustainability of maintaining such systems over long operational lifetimes. Through cross-domain comparisons with existing risk-aware frameworks such as constrained Markov decision processes and shielding-based methods, we demonstrate that SafePath-RL offers a scalable and auditable pathway toward trustworthy autonomy. The results underscore that deliberative reasoning, when appropriately coupled with reinforcement learning, can mitigate the brittleness of purely reactive agents without imposing prohibitive latency. This work contributes both a conceptual architecture and a set of design principles for building autonomous decision agents that are not only effective but also accountable.
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