Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset (2411.04034v1)

Abstract: Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violate this assumption are becoming more popular; examples include supervised learning under distributional shifts, reinforcement learning, continual learning and non-stationary contextual bandits. In this work we introduce a novel learning approach that automatically models and adapts to non-stationarity, via an Ornstein-Uhlenbeck process with an adaptive drift parameter. The adaptive drift tends to draw the parameters towards the initialisation distribution, so the approach can be understood as a form of soft parameter reset. We show empirically that our approach performs well in non-stationary supervised and off-policy reinforcement learning settings.

• The paper demonstrates that adaptive soft parameter reset improves neural network training efficiency in non-stationary contexts by dynamically updating parameters.
• The paper introduces an adaptive drift mechanism integrated with online SGD to alter learning rates and starting points for parameter updates.
• The paper validates its method through empirical experiments on benchmarks and reinforcement learning, showing enhanced plasticity and robust performance.

Overview of Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset

The paper introduces an innovative method to train neural networks (NNs) in non-stationary environments, where traditional assumptions of stationary data distributions do not hold. Specifically, it presents a novel approach using Automatic Soft Parameter Reset, underpinned by an Ornstein-Uhlenbeck (OU) process with an adaptive drift parameter. This work significantly contributes to the improvement of neural networks’ adaptability and learning efficiency in dynamic contexts such as continual learning and reinforcement learning.

Key Contributions

1. Adaptive Drift Mechanism:
   • The authors propose a soft reset mechanism that drifts NN parameters towards the initialization distribution while maintaining proximity to current parameter values. This is achieved through learning an adaptive drift parameter online, allowing for enhanced flexibility compared to more rigid hard-reset strategies.
2. Integration with Online Stochastic Gradient Descent (SGD):
   • The learning framework modifies the starting point for parameter updates and adjusts the learning rate based on the detected non-stationarity level. This integration utilizes the drift model to systematically alter the parameters before new data processing, akin to a dynamic Bayesian prior.
3. Empirical Validation:
   • The paper provides empirical evidence indicating that the proposed soft reset mechanism helps maintain plasticity in NNs and performs effectively in off-policy reinforcement learning scenarios. The results demonstrate superior adaptability and mitigation of plasticity loss compared to existing strategies.

Methodological Insights

The authors develop a dynamically adjusted Ornstein-Uhlenbeck process to guide parameter drift. This model, expressed through a Gaussian distribution, incorporates a learnable drift parameter that quantifies non-stationarity in data. The drift parameter impacts both the starting point adjustment and the learning rate enhancement—these adaptivity features make the framework robust across various degrees of data distribution shifts.

The update rules for parameters are formulated to combine the deterministic drift adjustments with the stochastic approximation derived from the current data batch. Specifically, the model utilizes the predicted likelihood to optimize the drift parameters and subsequently updates the NN parameters through a Bayesian-neural network framework to maintain balance between exploration (via increased learning rates) and exploitation (by preserving past learned information).

Experimental Evaluation

The paper evaluates the proposed method across multiple benchmarks such as permuted MNIST, random-label MNIST, and CIFAR-10. In particular, the experiments underline the method’s effectiveness in maintaining NN performance over sequential tasks—highlighting strengths in both data-efficient learning scenarios and memorization tasks. Importantly, the Bayesian variant of the method shows pronounced improvements, demonstrating the utility of enriched uncertainty quantification.

Additionally, the approach is tested under reinforcement learning conditions using Soft Actor-Critic (SAC), where it significantly mitigates the loss of plasticity in highly off-policy settings. The ability to dynamically adjust to changing reward landscapes underscores the practical applicability of the proposed mechanism.

Implications and Future Directions

This work introduces a nuanced understanding of leveraging drift models to counteract plasticity loss in neural networks within non-stationary environments. By learning when and how to perform soft resets, the framework effectively adapts to variations in data distributions. This approach holds implications for enhancing robust NN training in dynamic scenarios, potentially influencing methodologies in data streams, reinforcement learning, and beyond.

Future research could further investigate connections between the proposed soft reset mechanism and continual learning frameworks, examining whether these findings translate into broader theoretical advances. Additionally, extending the model to consider interactions between multiple networks or layers could enrich understanding of interdependencies within such adaptive systems.

Ultimately, the exploration of adaptive drift models as a mechanism for parameter updates in non-stationary learning continues to unfold, with this work contributing a pivotal step forward in harnessing neural network adaptability in evolving environments.