Improving Generalization in Reinforcement Learning with Mixture Regularization (2010.10814v1)

Abstract: Deep reinforcement learning (RL) agents trained in a limited set of environments tend to suffer overfitting and fail to generalize to unseen testing environments. To improve their generalizability, data augmentation approaches (e.g. cutout and random convolution) are previously explored to increase the data diversity. However, we find these approaches only locally perturb the observations regardless of the training environments, showing limited effectiveness on enhancing the data diversity and the generalization performance. In this work, we introduce a simple approach, named mixreg, which trains agents on a mixture of observations from different training environments and imposes linearity constraints on the observation interpolations and the supervision (e.g. associated reward) interpolations. Mixreg increases the data diversity more effectively and helps learn smoother policies. We verify its effectiveness on improving generalization by conducting extensive experiments on the large-scale Procgen benchmark. Results show mixreg outperforms the well-established baselines on unseen testing environments by a large margin. Mixreg is simple, effective and general. It can be applied to both policy-based and value-based RL algorithms. Code is available at https://github.com/kaixin96/mixreg .

• The paper proposes Mixreg, a novel method using mixed environmental observations and rewards to enhance data diversity and generalization in reinforcement learning, addressing overfitting.
• Mixreg trains RL agents by interpolating observations and rewards from different environments, promoting smoother policies and building on supervised learning's mixup idea.
• Empirical validation on Procgen shows Mixreg significantly outperforms previous data augmentation methods, integrating successfully with PPO and Rainbow DQN.

Essay: Improving Generalization in Reinforcement Learning with Mixture Regularization

The paper "Improving Generalization in Reinforcement Learning with Mixture Regularization" addresses the prevalent issue of overfitting in deep reinforcement learning (RL) agents when trained on a limited variety of environments. This overfitting impairs the agents' ability to generalize effectively to unseen environments, a major barrier to practical deployments of RL technologies.

Context and Motivations

The authors identify a key challenge in the reinforcement learning domain: RL agents typically train in static and homogeneous environments, leading to overfitting and suboptimal performance in novel situations. Previous approaches, such as data augmentation techniques like cutout and random convolution, inadvertently introduced only slight perturbations within the state feature space without broadening the scope of data diversity significantly. Consequently, these methods exhibited only marginal improvements in the generalization capabilities of agents.

The Mixreg Approach

Responding to these limitations, the authors propose a novel technique called "Mixreg," designed to enhance data diversity more effectively. Mixreg operates by training RL agents using a mix of observations from various environments, thereby extending the diversity of training data and imposing linearity constraints both on observation interpolations and on the associated rewards. This practice enables the agents to learn smoother policies, demonstrated to promote better generalization performance.

Mixreg builds on the idea of mixup from supervised learning. By blending observations and their corresponding rewards from different environments, it effectively bolsters data diversity and smoothes policy transitions, which is crucial in creating robust and adaptable RL systems. Mixreg's application is straightforward and is compatible with both policy-based and value-based RL methods.

Empirical Validation and Results

The authors rigorously tested the efficacy of Mixreg against established benchmarks using the Procgen benchmark, known for its large scale and robustness in evaluating generalization in RL. The experiments reveal that Mixreg significantly outperformed several prominent data augmentation techniques and regularization methods like batch normalization and $\ell_2$ regularization. Notably, when employed alongside $\ell_2$ regularization, Mixreg achieved further enhancements in performance.

Through extensive experimentation, the paper also confirms Mixreg's versatility, demonstrating its successful integration into both policy gradient and deep Q-learning frameworks, specifically Proximal Policy Optimization (PPO) and Rainbow DQN, respectively.

Theoretical and Practical Implications

From a theoretical standpoint, Mixreg underscores the importance of diverse data sampling paired with thoughtful regularization in the context of RL. This combination not only enhances generalization but also supports the development of more nuanced policies. Practically, the insights from this work could significantly influence future RL strategies, ushering methods that lean heavily on environment diversity and reward-based interpolations.

Future Directions

The paper sets a precedent for subsequent studies to explore more complex mixing schemes, expanding beyond uniform distributions or fixed interpolation methods. Going forward, researchers could target further optimization by dynamically adjusting mixing parameters or applying Mixreg in domains affected by different types of environmental variability, such as dynamics or structural changes.

In conclusion, "Improving Generalization in Reinforcement Learning with Mixture Regularization" makes a substantial contribution to the body of knowledge on reinforcement learning. By enhancing the robustness to unseen environments, Mixreg paves the way for more reliable and adaptable RL solutions suitable for a broader range of real-world applications.