Policy Gradient for Continuous-Time Robust Markov Decision Processes
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 differential equations. We propose double-loop optimisers to obtain linear convergence in the oracle-based setting and an $\tilde{\mathcal{O}}(\frac{1}...
| Authors | Tanya Veeravalli, David M. Bossens, Atsushi Nitanda |
|---|---|
| Primary category | cs.LG |
| Published | 3 June 2026 |
| Last updated at source | 4 June 2026 |
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