Representation Convergence: Mutual Distillation is Secretly a Form of Regularization
This work provides theoretical and empirical insights into generalization mechanisms in reinforcement learning, though it is incremental as it focuses on understanding rather than achieving state-of-the-art performance.
The paper tackles the problem of overfitting to irrelevant features in reinforcement learning by showing that mutual distillation between policies acts as implicit regularization, leading to improved generalization and the emergence of invariant representations over pixel inputs.
In this paper, we argue that mutual distillation between reinforcement learning policies serves as an implicit regularization, preventing them from overfitting to irrelevant features. We highlight two separate contributions: (i) Theoretically, for the first time, we prove that enhancing the policy robustness to irrelevant features leads to improved generalization performance. (ii) Empirically, we demonstrate that mutual distillation between policies contributes to such robustness, enabling the spontaneous emergence of invariant representations over pixel inputs. Ultimately, we do not claim to achieve state-of-the-art performance but rather focus on uncovering the underlying principles of generalization and deepening our understanding of its mechanisms.