Quantifying Generalization in Reinforcement Learning (1812.02341v3)

Abstract: In this paper, we investigate the problem of overfitting in deep reinforcement learning. Among the most common benchmarks in RL, it is customary to use the same environments for both training and testing. This practice offers relatively little insight into an agent's ability to generalize. We address this issue by using procedurally generated environments to construct distinct training and test sets. Most notably, we introduce a new environment called CoinRun, designed as a benchmark for generalization in RL. Using CoinRun, we find that agents overfit to surprisingly large training sets. We then show that deeper convolutional architectures improve generalization, as do methods traditionally found in supervised learning, including L2 regularization, dropout, data augmentation and batch normalization.

• The paper introduces CoinRun, a new benchmark that uncovers significant overfitting in traditional RL training environments.
• It demonstrates that deeper convolutional architectures with techniques like L2 regularization and dropout enhance generalization.
• The study establishes that procedural generation combined with stochastic regularization offers a robust framework for evaluating RL performance.

Quantifying Generalization in Reinforcement Learning

In the paper "Quantifying Generalization in Reinforcement Learning," Cobbe et al. investigate the persistent challenge of overfitting in deep reinforcement learning (RL) and address the inadequacies of using the same environments for both training and testing. The research introduces procedurally generated environments as a means to obtain separate training and test sets, thus providing more accurate insights into an agent's generalization capabilities.

Main Contributions

The paper presents several key contributions:

1. CoinRun Environment: A new environment called CoinRun is introduced, specifically designed to benchmark generalization in RL. The paper reveals significant overfitting, even with larger-than-expected training sets.
2. Architectural Insights: The research demonstrates that deeper convolutional architectures enhance generalization. This is complemented by techniques from supervised learning, such as L2 regularization, dropout, data augmentation, and batch normalization, all contributing to improved performance.
3. Procedural Generation and Metrics: The authors propose using procedurally generated benchmarks to quantify generalization, offering a metric that facilitates iterative improvements in RL algorithms.

Analysis and Results

The paper employs the CoinRun environment, where agents exhibit overfitting across various training set sizes. It is observed that when trained on an unbounded set of levels, the agents' ability to generalize significantly improves. Using convolutional architectures like IMPALA-CNN, agents exhibit enhanced generalization over baselines such as Nature-CNN, suggesting that architectural innovations play a crucial role in tackling overfitting.

Various forms of regularization are examined. L2 regularization and dropout are effective in reducing the generalization gap, with optimal results at specific levels. Data augmentation, implemented via a modified Cutout methodology, offers tangible improvements, further reinforced by batch normalization.

Furthermore, the introduction of stochasticity, both in the form of epsilon-greedy action selection and entropy bonuses, is shown to significantly enhance generalization, illustrating how stochastic methods can counteract overfitting in deterministic environments.

Theoretical and Practical Implications

The research underlines the necessity for improved generalization in RL algorithms to better approximate human-like task flexibility. The introduction of environments like CoinRun establishes a valuable benchmark for forthcoming research, providing a structured methodology for evaluating RL agents' ability to generalize. These findings prompt further investigation into architectural modifications and the integration of regularization techniques from supervised learning to enhance generalization.

Prospects for Future Research

Future work could involve experimenting with different recurrent architectures to evaluate their impact on generalization in environments requiring memory and exploration. Moreover, the exploration of larger and more diverse sets of procedural environments could offer deeper insights into the scalability and adaptability of RL algorithms.

In conclusion, this paper offers significant insights into the generalization challenges in RL and provides a rigorous framework through procedural generation to evaluate and improve RL agents' performance across diverse environments.