A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning (1806.07937v2)

Abstract: The risks and perils of overfitting in machine learning are well known. However most of the treatment of this, including diagnostic tools and remedies, was developed for the supervised learning case. In this work, we aim to offer new perspectives on the characterization and prevention of overfitting in deep Reinforcement Learning (RL) methods, with a particular focus on continuous domains. We examine several aspects, such as how to define and diagnose overfitting in MDPs, and how to reduce risks by injecting sufficient training diversity. This work complements recent findings on the brittleness of deep RL methods and offers practical observations for RL researchers and practitioners.

• The paper systematically explores overfitting and generalization in continuous deep reinforcement learning using randomized experiments, finding that increasing training data diversity significantly improves generalization.
• The study used randomization experiments in various RL environments, demonstrating that environments requiring more diversity to avoid overfitting include those with pixels and complex continuous control.
• Findings suggest future RL research and benchmarks should focus on incorporating greater environmental variability and diversity to improve real-world generalization and transfer learning.

Dissecting Overfitting and Generalization in Continuous Reinforcement Learning

The paper "A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning" investigates overfitting in the context of deep reinforcement learning (RL), focusing on tasks with continuous domains. This exploration is pertinent as most diagnostics and solutions for overfitting have been developed within the supervised learning (SL) paradigm. Overfitting, when a model learns to memorize training data rather than generalizing from it, can lead to poor performance on unseen data, posing risks in RL where adaptation to various states is crucial.

Generalization in Deep RL

RL systems often interact with finite, deterministic simulators, where fixed seeds can lead to predictable outcomes. This deterministic nature exacerbates the risk of overfitting, particularly in domains with limited variability, such as those with small state spaces or deterministic transitions. The authors of this work systematically explore memorization and generalization issues, providing an analysis of overfitting through a series of randomized reward and state experiments across various environments.

Methodological Approaches

The investigation includes both model-free and model-based approaches, examining discrete and continuous action spaces. Notably, randomization experiments are used in both setups:

• Within-task Generalization: The effect of the number of training seeds on overfitting was explored, with experiments in common environments like Cartpole, Acrobot, and others implemented in Gym. They found that increased diversity from more training seeds leads to better generalization in RL, suggesting that training seed variance is critical for reducing overfitting.
• Out-of-task Generalization: This was tested by altering environmental dynamics, such as modifying initial states or adding Gaussian noise. Such strategies were pivotal in testing the robustness of the generalization capacities of RL systems.

Key insights were drawn from tasks interfacing with natural images, such as MNIST and CIFAR10, highlighting that RL systems require substantially more diversity to avoid overfitting compared to simpler synthetic tasks.

Numerical Results

The research provides robust numerical analysis:

• In Cartpole's pixel domain, significant overfitting was observed with a small training seed count, whereas the introduction of randomness in rewards revealed memorization tendencies in RL systems.
• Experiments in the continuous control settings, such as the MuJoCo environments, illustrated that increased task complexity (e.g., ThrowerMulti) demands larger training diversities for effective generalization.
• Despite controlled randomization, performance generally remains robust with a sufficient number of diverse training seeds.

Implications and Future Directions

The implications of these findings are multifaceted:

1. Transfer Learning: The research highlights the necessity to create RL systems capable of generalizing across task variations and noise instigators, offering insights into improving transfer learning methodologies.
2. Benchmarks and Testing: It calls for the development of more robust benchmarks simulating real-world noise and complexity, as traditional benchmarks often lack sufficient variability.
3. Research Practice: The results encourage RL researchers to adopt practices that mitigate overfitting, such as leveraging diverse data sources or enhancing variance in simulator conditions.

The paper lays groundwork for addressing overfitting through practical experiments and thoughtful insights, serving as an essential resource for advancing generalization objectives in RL. Future explorations might focus on expanding these principles into more nuanced domains or integrating them with advanced neural architectures. This research is foundational for crafting RL systems that not only excel in simulations but are capable of robust performance in the unpredictable real world.