Generalization and Regularization in DQN (1810.00123v3)

Abstract: Deep reinforcement learning algorithms have shown an impressive ability to learn complex control policies in high-dimensional tasks. However, despite the ever-increasing performance on popular benchmarks, policies learned by deep reinforcement learning algorithms can struggle to generalize when evaluated in remarkably similar environments. In this paper we propose a protocol to evaluate generalization in reinforcement learning through different modes of Atari 2600 games. With that protocol we assess the generalization capabilities of DQN, one of the most traditional deep reinforcement learning algorithms, and we provide evidence suggesting that DQN overspecializes to the training environment. We then comprehensively evaluate the impact of dropout and $\ell_2$ regularization, as well as the impact of reusing learned representations to improve the generalization capabilities of DQN. Despite regularization being largely underutilized in deep reinforcement learning, we show that it can, in fact, help DQN learn more general features. These features can be reused and fine-tuned on similar tasks, considerably improving DQN's sample efficiency.

Generalization and Regularization in Deep Q-Networks

The paper "Generalization and Regularization in DQN" addresses a critical challenge in deep reinforcement learning (RL): the lack of generalization in the policies learned by deep Q-Networks (DQN). Despite the robust performance of deep RL algorithms on high-dimensional control tasks, these learned policies often struggle to transfer across environments that differ only slightly from the training setup.

Key Contributions

The authors embark on a comprehensive investigation into whether learned representations in deep RL can generalize beyond the training environment. They propose the following contributions to the paper of generalization in reinforcement learning:

1. Evaluation Protocol for Generalization: The introduction of an evaluation protocol based on different modes of Atari 2600 games to assess generalization in RL. The idea is to train an agent in one flavor or mode of a game and evaluate the learned policy in another mode.
2. Assessment of DQN's Generalization Capabilities: The paper highlights the over-specialization tendency of DQN. By testing a DQN trained on default game modes against variant modes, the authors provide empirical evidence of the limited transferability of its learned policies.
3. Impact of Regularization Techniques: The research explores the impact of conventional regularization methods such as dropout and $\ell_2$ regularization. These techniques, although underutilized in deep RL, show potential in improving the generalization abilities of DQN by encouraging the learning of more general features.
4. Representation Reusability and Fine-tuning: By employing fine-tuning of regularized representations, the paper demonstrates improved sample efficiency in target environments, suggesting improved adaptability.

Findings

The results reveal that policies learned by DQN lack robustness when exposed to new variants of the training environment. Specifically, despite visual and dynamic similarities between game modes, the transfer of policies is notably ineffective. The authors observe patterns of overfitting, particularly in the Freeway game, where initial improvements transition into performance degradation as the agent specializes excessively on the training flavor.

Regularization as a Mitigation Strategy

Dropout and $\ell_2$ regularization were evaluated as mechanisms for discouraging overfitting. These techniques significantly improve the sample efficiency and initial performance of DQN in new flavors by promoting the learning of more adaptable representations. Indeed, fine-tuning regularized networks across different flavors led to notable performance improvements, thereby validating the hypothesis that regularization aids in learning representations with better generalization to new tasks.

Implications and Future Directions

The findings indicate compelling evidence that applying regularization techniques can effectively counteract overfitting in deep RL, providing a pathway towards more generalizable agents. This research underlines the importance of studying generalization within RL, emphasizing the utility of transferable and adaptable representations.

Future work can build upon these insights by exploring more sophisticated regularization techniques and integrating meta-learning approaches to further enhance the robustness of learned policies. Additionally, extending this line of investigation to a broader range of environments could help define new paradigms for evaluating and achieving generalization in RL.

In conclusion, this paper presents critical insights into the limitations and potential solutions for generalization in DQN, fostering a deeper understanding of how to craft RL agents that can generalize beyond their training environments.