arXiv:2606.04335v1 Announce Type: new Abstract: The framework of robust Markov decision processes (RMDPs) allows the design of reinforcement learning agents that satisfy performance guarantees under worst-case transition dynamics. Traditional RMDPs consider discrete-time dynamics and recently, sample-efficient policy gradient algorithms have been considered in this context. This paper investigates policy gradient algorithms within a continuous-time RMDP framework. Policy gradients and adversarial gradients are derived using pathwise and adjoint-based formulas for stochastic and ordinary differ
The accelerating pace of AI development and deployment necessitates more robust and adaptable control policies, especially in complex, real-world environments.
This research provides a foundational step towards more resilient and trustworthy AI agents that can operate effectively under uncertainty by addressing worst-case scenarios.
AI systems will gain enhanced capabilities for operating in dynamic and unpredictable environments with stronger performance guarantees, moving beyond discrete-time limitations.
- · AI development firms
- · Robotics sector
- · Defence and industrial automation
- · Reinforcement learning researchers
- · Systems relying on brittle, non-robust AI
- · Traditional control engineering approaches
More reliable and adaptable AI agents become feasible across various applications.
Increased trust in autonomous systems, leading to wider adoption in critical sectors.
New competitive landscapes emerge, favoring organizations that can integrate robust AI for operational resilience and strategic advantage.
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Read at arXiv cs.LG