Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning (1910.05396v3)

Abstract: Deep reinforcement learning (RL) agents often fail to generalize to unseen environments (yet semantically similar to trained agents), particularly when they are trained on high-dimensional state spaces, such as images. In this paper, we propose a simple technique to improve a generalization ability of deep RL agents by introducing a randomized (convolutional) neural network that randomly perturbs input observations. It enables trained agents to adapt to new domains by learning robust features invariant across varied and randomized environments. Furthermore, we consider an inference method based on the Monte Carlo approximation to reduce the variance induced by this randomization. We demonstrate the superiority of our method across 2D CoinRun, 3D DeepMind Lab exploration and 3D robotics control tasks: it significantly outperforms various regularization and data augmentation methods for the same purpose.

• The paper introduces randomized input perturbation using convolutional layers to enhance deep RL agents' invariant feature learning.
• The authors apply Monte Carlo approximation during inference to stabilize performance amid input randomness.
• Empirical results show significant improvements, with CoinRun success rates rising from 39.8% to 58.7% compared to existing methods.

Overview of "A Simple Randomization Technique for Generalization for Visual Perception in Deep Reinforcement Learning"

The paper by Kimin Lee et al. proposes a straightforward yet effective method to enhance the generalization capacity of deep reinforcement learning (RL) agents confronted with high-dimensional state spaces such as images. The prevalent challenge these agents face is their limited ability to generalize to new, semantically similar environments they have not previously encountered. The authors introduce a novel approach leveraging a randomized convolutional network to effectively perturb input observations, thus promoting robust feature learning across varied visual patterns.

Key Contributions and Methods

1. Randomized Input Perturbation: The authors implement a random convolutional layer that modifies input observations through randomized perturbation. By frequently reinitializing this layer's parameters during training, the agents encounter a diverse spectrum of perturbed input features, thereby fostering representation learning that is invariant across different visual contexts.
2. Monte Carlo Approximation for Inference: To mitigate the variance caused by randomization at test time, an inference technique based on the Monte Carlo approximation is introduced. This method improves performance stability by yielding more precise expectations of the policy from randomized inputs.
3. Empirical Verification: The approach was validated on multiple benchmarks, including 2D CoinRun, 3D DeepMind Lab, and 3D robotics control tasks. The method demonstrated superior generalization across unseen environments compared to existing regularization and data augmentation strategies. Notably, success rates for CoinRun improved substantially from 39.8% to 58.7%, underscoring the effectiveness of the proposed technique.
4. Robustness and Applicability: Beyond enhancing generalization, the approach also implicitly contributes to robustness against adversarial perturbations, as evidenced by its ability to maintain performance under adversarially modified inputs.

Implications and Future Directions

The implications of this work are twofold. Practically, the proposed method offers a broadly applicable approach for deploying RL agents in environments characterized by high variability, such as real-world applications in robotics and autonomous systems. Theoretically, this method highlights the potential of using random networks not just for exploration, but as a means to enhance generalization of learned policies across diverse input distributions.

As AI continues to be integrated into complex real-world applications, ensuring that models generalize well to new environments without extensive retraining is critical. The simplicity and effectiveness of this method pave the way for further exploration into domain-agnostic strategies that leverage stochastic representations to bolster generalization.

Future research could explore extending this method to other aspects of reinforcement learning, such as varying dynamic environments, and merging it with other state-of-the-art techniques in domains like transfer learning, continual learning, and especially sim-to-real transfer scenarios. Moreover, the interplay between random perturbations and the stability of learning dynamics warrants deeper investigation, which might uncover new insights into balancing exploration, exploitation, and generalization in reinforcement learning frameworks.