Unlocking Predictive Power: Adapting Models for Downstream Tasks

Deep reinforcement learning (DRL) aims to train agents to make decisions that maximize rewards in complex environments. However, DRL models often struggle with tasks that require them to focus on different aspects of the data. To address this challenge, researchers have proposed using task-based loss functions, which modify the standard loss function used in DRL to better align the model’s predictions with the task at hand.
One approach is to use a separate metric for each task, such as mean squared error (MSE) or binary cross-entropy, and optimize the model using these metrics directly. This allows the model to specialize its predictions to each task, leading to improved performance on downstream tasks. However, this approach can be computationally expensive and may not scale well for large datasets.
Another approach is to use a single metric that captures the overall performance of the model across multiple tasks. In this case, the loss function is modified to include a task-based weighting term that adjusts the importance of each task based on its relevance to the current task. This allows the model to focus more on tasks that are relevant to the current task, leading to improved performance on downstream tasks.
There are also works that use task loss in a different way compared to the above methods. They use task loss as a weighting term in the MSE loss itself, so the models are trained to focus more on samples with higher task loss. In their work, the task is the estimation of the value function in model-based RL. This can be seen as the instantiation of our work where the task loss is directly used as a metric instead of learning a metric.
In summary, task-based loss functions are a promising approach to improve the performance of DRL models on complex tasks by aligning their predictions with the task at hand. By using separate metrics or weighting terms for each task, these methods can help the model specialize its predictions to each task, leading to improved performance on downstream tasks.