To make the algorithm more robust to uncertainty in the delay process when the delay values are not directly observed by the agent, we can implement a strategy to handle the variability and unpredictability of delays. One approach could involve incorporating a mechanism for estimating delay values based on historical data or patterns in the delay process. By leveraging machine learning techniques such as time series analysis or predictive modeling, the algorithm can learn to predict and adapt to varying delay values without direct observation.
Additionally, introducing a mechanism for adaptive decision-making based on the estimated delay values can help the algorithm adjust its actions in real-time to account for uncertainty in the delay process. By continuously monitoring and updating its predictions of delay values, the algorithm can make more informed decisions and mitigate the impact of uncertainty on its performance.