Yes, self-supervised learning (SSL) can be effectively integrated with reinforcement learning (RL) to improve performance, especially in environments where labeled data is scarce or costly to obtain. SSL enables RL agents to learn useful representations from raw, unlabeled data by creating auxiliary tasks that exploit the structure of the data itself. For example, an agent might predict future states in a video frame or reconstruct masked portions of sensor inputs. These tasks help the agent build a richer understanding of the environment, which can accelerate learning and improve sample efficiency—the ability to learn effectively from fewer interactions. By pre-training or jointly training with SSL, RL agents can develop generalizable features that reduce dependency on reward signals, which are often sparse or delayed in real-world scenarios.
A practical example of SSL in RL is its use in Atari game playing. Agents trained with SSL might learn to predict the next frame in a game sequence or classify whether two augmented views of a frame belong to the same observation. Methods like CURL (Contrastive Unsupervised Representations for Reinforcement Learning) apply contrastive learning to align latent representations of similar states, improving the agent’s ability to distinguish between meaningful patterns in pixel data. Another example is model-based RL, where SSL helps build a world model by predicting future states and rewards based on current actions. For instance, the Dreamer algorithm trains a dynamics model using SSL-style prediction tasks, allowing the agent to simulate and plan over imagined trajectories without direct interaction, reducing the need for costly environment steps.
However, integrating SSL with RL requires careful design. SSL tasks must align with the RL objective to avoid learning irrelevant features. For example, predicting random noise in images might not aid an agent’s decision-making. Computational overhead is another consideration: SSL can increase training time due to additional prediction tasks. Developers should start with simple SSL objectives, such as state reconstruction or temporal consistency, and validate that learned features improve policy performance. Balancing exploration (trying new actions) and exploitation (using known strategies) also becomes more complex when SSL introduces new learning signals. Despite these challenges, combining SSL with RL offers a promising path for agents to generalize better and adapt to complex, high-dimensional environments with minimal supervision.