Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes (2006.11441v3)

Abstract: Continuously learning to solve unseen tasks with limited experience has been extensively pursued in meta-learning and continual learning, but with restricted assumptions such as accessible task distributions, independently and identically distributed tasks, and clear task delineations. However, real-world physical tasks frequently violate these assumptions, resulting in performance degradation. This paper proposes a continual online model-based reinforcement learning approach that does not require pre-training to solve task-agnostic problems with unknown task boundaries. We maintain a mixture of experts to handle nonstationarity, and represent each different type of dynamics with a Gaussian Process to efficiently leverage collected data and expressively model uncertainty. We propose a transition prior to account for the temporal dependencies in streaming data and update the mixture online via sequential variational inference. Our approach reliably handles the task distribution shift by generating new models for never-before-seen dynamics and reusing old models for previously seen dynamics. In experiments, our approach outperforms alternative methods in non-stationary tasks, including classic control with changing dynamics and decision making in different driving scenarios.

Continual Online Model-Based Reinforcement Learning with an Infinite Mixture Model Framework

Introduction of Methods and Main Contributions

The quest to imbue machines with the human-like ability to adapt quickly to unforeseen situations has been a long-standing goal in machine learning. Traditional approaches in meta-learning and continual learning have made strides toward this objective yet often fall short in real-world scenarios where the dynamics can change unpredictably, task distributions are complex, and clear task delineations are not available. Addressing these limitations requires a paradigm shift in how models are trained and updated. Herein, the work by Xu et al. introduces a novel model-based reinforcement learning (RL) method that adeptly handles non-stationary tasks without relying on pre-defined task distributions or pretraining. Central to their approach is the utilization of an infinite mixture of Gaussian Processes (GPs) to model the dynamical systems, enabling efficient data utilization and fast adaptation to new tasks.

Their main contributions can be highlighted as follows:

• Introduction of a novel model-based RL method: The method uses an infinite mixture model framework of Gaussian Processes for online learning in non-stationary environments. Unlike many existing methods, it does not require pre-training or predefined task boundaries.
• Efficient handling of dynamic task transitions: By maintaining a mixture of experts within the model, the approach effectively manages the task distribution shift, dynamically generating new models for unseen dynamics and reinstating old models for previously encountered tasks.
• Theoretical formulation and algorithm for scalable online Bayesian inference: Employing sequential variational inference with a transition prior allows the method to efficiently learn and update the mixture model online, taking into account temporal dependencies in sequential data.
• Demonstration through experiments: The method outperforms alternative approaches in various non-stationary tasks, evidenced by stronger numerical results and the ability to reliably detect shifts in task dynamics.

Principled Approach and Technical Insights

At the heart of their approach is the strategic use of Gaussian Processes (GPs) to model each type of environmental dynamics encountered by the RL agent. The choice of GPs is pivotal due to their capacity for efficient data use, expressiveness in modeling uncertainty, and inherent adaptability to novel tasks. The extension to an infinite mixture model framework ensures that the approach can handle an unknown number of tasks without a priori knowledge of task distribution or boundaries.

The model-based RL method exploits Model Predictive Control (MPC) for action selection, estimating future state trajectories using the learned dynamics model. This closed-loop control mechanism enhances the method's robustness against model inaccuracies.

A notable technical advancement is the introduction of a transition prior within the sequential variational inference scheme. This prior explicitly accounts for the temporal coherence typical in real-world scenarios, where consecutive observations are more likely to be related. It thus improves the detection of shifts between different dynamical systems, ensuring smoother transitions and better adaptation.

Data efficiency and scalability are addressed through data distillation using inducing points, enabling the method to maintain performance while dealing with extensive streaming data. Furthermore, the authors introduce an expert merge and prune mechanism to manage the model's complexity, merging redundant dynamics models and pruning unstable ones to keep the mixture model concise and focused.

Future Directions and Considerations

This work opens up numerous avenues for further research. One immediate direction is the exploration of integrating meta-learning principles within this framework to enhance the shared knowledge across different tasks further. Another promising area is the adaptation of Neural Processes as an alternative to GPs, potentially offering improved scalability and flexibility.

Moreover, while this method demonstrates significant advantages in handling non-stationary environments, exploring its applicability and performance in even more complex real-world scenarios would be valuable. For instance, investigating its efficacy in multi-agent systems or in environments with highly stochastic dynamics could reveal additional insights and potential improvements.

Summation

The method introduced by Xu et al. sets a new benchmark for model-based reinforcement learning in non-stationary environments. By elegantly combining the strengths of Gaussian Processes, infinite mixture models, and online Bayesian inference, it showcases remarkable adaptability and performance. This work not only advances the state-of-the-art in adaptive, continual learning for RL agents but also lays a solid foundation for future explorations in creating more robust and versatile AI systems capable of tackling the dynamic challenges of the real world.