In reinforcement learning, reward models are critical as they provide the feedback signal that agents use to learn optimal behaviors. However, relying on a single reward model can be problematic due to noise, biases, or inaccuracies inherent in the model. A reward model ensemble addresses these issues by aggregating the outputs of several reward models, each trained under different conditions or using diverse methods. This ensemble approach helps to smooth out the errors of individual models, leading to more reliable and consistent reward signals. By leveraging the strengths and compensating for the weaknesses of individual models, ensembles enhance the stability and performance of the learning process. This method is particularly valuable in complex environments where single reward models may struggle to capture all nuances of the optimal reward structure.
The concept of using ensembles in machine learning dates back to the 1990s, with significant advancements in the 2000s. The specific application of ensembles to reward models in reinforcement learning became more prominent in the 2010s, paralleling the growth of deep reinforcement learning techniques and the increasing complexity of tasks these agents were applied to.
Significant contributors to the development of ensemble methods in machine learning include Leo Breiman, who introduced the bagging and random forest methods, and Thomas G. Dietterich, who extensively studied ensemble learning. In the context of reinforcement learning, researchers like Pieter Abbeel and Andrew Ng have been influential, particularly in integrating sophisticated model-based and model-free approaches that can benefit from ensemble techniques.