Hi Mariam,
Cross-validation is a widely used technique in machine learning to evaluate the performance of models. However, the traditional cross-validation method, such as k-fold cross-validation, is more commonly applied to supervised learning tasks. In the context of reinforcement learning (RL) and agents like Deep Deterministic Policy Gradient (DDPG), the evaluation strategy differs because these models learn from interactions with an environment rather than from a fixed dataset.
For evaluating a DDPG agent, especially in a specific application like controlling osmotic pressure in a reverse osmosis station, you can approach testing the agent in the environment under different scenarios like:
- You can try splitting experiences or episodes, instead of splitting data. Example, you can set aside a part of the environment’s scenarios or configurations as a validation set and test them on the same to evaluate the performance without training them on the same.
- You can run your training process multiple times with different random seeds. Each seed will lead to a different sequence of experiences (due to the stochastic nature of most environments and exploration strategies), which helps in assessing the robustness of your agent.
- You can create various scenarios that can occur in real-life operations, including common, rare, and extreme conditions. After creation, you can test your agent across these scenarios to evaluate the robustness and performance of the model.
While the above strategies are not cross-validation in the traditional sense, they serve a similar purpose in the context of RL: to evaluate the agent's ability to generalize and perform well across a range of scenarios. Since RL involves learning policies that interact with an environment, the focus is on how well the agent adapts to the environment's dynamics rather than how it performs on a static set of data.
Hope this helps.
