Can SSL be used in reinforcement learning for evaluation purposes?

Yes, SSL (self-supervised learning) can be effectively integrated into reinforcement learning (RL) for evaluation purposes. SSL focuses on learning useful representations from unlabeled data by creating proxy tasks, such as predicting missing parts of input data or contrasting similar and dissimilar samples. In RL, where agents learn through trial and error in an environment, SSL can enhance evaluation by providing richer representations of states or actions. These representations help measure an agent’s performance more robustly, especially when explicit rewards are sparse or noisy. For example, SSL-trained models can extract features that capture underlying environment dynamics, enabling better comparisons between policies or agents during evaluation phases.

One practical application is using SSL to pre-train encoder networks that process raw observations (e.g., pixels in a robot’s camera feed) into compact state representations. These representations can then be used to evaluate how well an RL agent generalizes across tasks. For instance, in a navigation task, an SSL model could learn to predict depth or object positions from images without manual labels. During evaluation, the agent’s policy could be tested on its ability to reach goals in unseen environments, with SSL-derived metrics measuring consistency in the learned representations. Similarly, contrastive SSL methods could help distinguish between high-value and low-value states in a game-playing agent, providing a basis for evaluating whether the agent prioritizes meaningful states during testing.

However, integrating SSL into RL evaluation requires careful design. SSL objectives must align with the RL task’s goals to avoid misleading metrics. For example, an SSL task that predicts future states might not directly correlate with an agent’s reward-seeking behavior. Developers should also consider computational overhead: SSL pre-training adds initial training time, though it may reduce the need for extensive environment interactions later. A case study in Atari game evaluation showed SSL-based representations improved sample efficiency by 30% when fine-tuning policies. While SSL isn’t a universal solution, it offers a valuable tool for creating more informative evaluation frameworks in RL, particularly in complex or partially observable environments where traditional reward signals are insufficient.