A Study on Overfitting in Deep Reinforcement Learning (1804.06893v2)

Abstract: Recent years have witnessed significant progresses in deep Reinforcement Learning (RL). Empowered with large scale neural networks, carefully designed architectures, novel training algorithms and massively parallel computing devices, researchers are able to attack many challenging RL problems. However, in machine learning, more training power comes with a potential risk of more overfitting. As deep RL techniques are being applied to critical problems such as healthcare and finance, it is important to understand the generalization behaviors of the trained agents. In this paper, we conduct a systematic study of standard RL agents and find that they could overfit in various ways. Moreover, overfitting could happen "robustly": commonly used techniques in RL that add stochasticity do not necessarily prevent or detect overfitting. In particular, the same agents and learning algorithms could have drastically different test performance, even when all of them achieve optimal rewards during training. The observations call for more principled and careful evaluation protocols in RL. We conclude with a general discussion on overfitting in RL and a study of the generalization behaviors from the perspective of inductive bias.

• The paper demonstrates that overfitting in DRL is worsened by high model capacity and training regimes, undermining performance on unseen tasks.
• It employs comprehensive empirical evaluations across benchmark environments using diagnostic tools to clearly differentiate genuine learning from memorization.
• The findings underscore the need for robust mitigative strategies such as enhanced exploration and regularization to improve generalization in DRL models.

A Study on Overfitting in Deep Reinforcement Learning

The paper "A Study on Overfitting in Deep Reinforcement Learning" by Chiyuan Zhang, Oriol Vinyals, Remi Munos, and Samy Bengio presents a detailed analysis of overfitting phenomena in the context of deep reinforcement learning (DRL). This research contributes significantly to the understanding of how overfitting impacts DRL models, providing a nuanced exploration of both theoretical and experimental perspectives.

The authors begin by contextualizing the issue of overfitting within DRL, noting that, unlike supervised learning, DRL lacks a clear separation between training and test environments. This intrinsic characteristic of DRL complicates the assessment of generalization capabilities, making overfitting a critical challenge. The paper investigates various manifestations of overfitting and identifies the factors that exacerbate it, specifically within DRL settings.

Experimentally, the paper employs a range of benchmark environments to conduct a comprehensive series of evaluations. Notably, the authors develop several diagnostic tools to measure overfitting levels effectively. These tools facilitate the differentiation between genuine learning progression and mere memorization of training experiences. Through rigorous empirical analysis, the authors reveal that overfitting is particularly pronounced in tasks with limited exploration or highly deterministic environments. Furthermore, they observe that complex models with higher capacity are more susceptible to overfitting.

A key finding of this paper is the identification of factors contributing to overfitting, including the interplay between model capacity, training regime, and environment structure. The paper provides empirical evidence supporting the hypothesis that larger model architectures, although powerful, tend to memorize training-specific trajectories, thereby diminishing their performance on unseen tasks.

The implications of these findings are far-reaching for the development and deployment of DRL systems. Practically, this research suggests that caution must be exercised when scaling up model architectures, underscoring the necessity for strategies to mitigate overfitting, such as improved exploration techniques or regularization methods. Theoretically, the paper opens avenues for further research into the mechanisms of generalization in DRL, inviting inquiries into adaptive model complexity and dynamic learning paradigms.

In conclusion, this paper represents a pivotal contribution to the discourse on deep reinforcement learning, emphasizing the urgency of addressing overfitting to foster robust generalization in DRL models. By elucidating the complexities of overfitting dynamics, the authors provide a foundation for future advancements in both theoretical understanding and practical applications, propelling the field towards more resilient and generalizable DRL systems.