Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck (1910.12911v1)

Abstract: The ability for policies to generalize to new environments is key to the broad application of RL agents. A promising approach to prevent an agent's policy from overfitting to a limited set of training environments is to apply regularization techniques originally developed for supervised learning. However, there are stark differences between supervised learning and RL. We discuss those differences and propose modifications to existing regularization techniques in order to better adapt them to RL. In particular, we focus on regularization techniques relying on the injection of noise into the learned function, a family that includes some of the most widely used approaches such as Dropout and Batch Normalization. To adapt them to RL, we propose Selective Noise Injection (SNI), which maintains the regularizing effect the injected noise has, while mitigating the adverse effects it has on the gradient quality. Furthermore, we demonstrate that the Information Bottleneck (IB) is a particularly well suited regularization technique for RL as it is effective in the low-data regime encountered early on in training RL agents. Combining the IB with SNI, we significantly outperform current state of the art results, including on the recently proposed generalization benchmark Coinrun.

• The paper introduces Selective Noise Injection (SNI) and Information Bottleneck Actor Critic (IBAC) as novel regularization methods tailored for enhancing generalization in reinforcement learning agents.
• Selective Noise Injection (SNI) improves training stability by selectively applying stochasticity, while Information Bottleneck Actor Critic (IBAC) encourages learning compressed, relevant features.
• Experiments demonstrate that combining IBAC and SNI significantly outperforms state-of-the-art methods on tasks requiring generalization, suggesting practical improvements for real-world RL deployment.

Insights into Generalization in Reinforcement Learning via Selective Noise Injection and Information Bottleneck

The paper "Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck" introduces novel methodologies aimed at enhancing the generalization capabilities of reinforcement learning (RL) agents. The central premise of the paper is that adaptive regularization techniques, specifically designed to address the inherent differences between supervised learning and RL, can significantly bolster an agent's ability to generalize across different environments.

Core Contributions

The authors explore two primary modifications to existing regularization techniques to better suit RL scenarios:

1. Selective Noise Injection (SNI): This methodology is designed to resolve some of the detrimental effects caused by stochastic regularization techniques, which can destabilize RL training because the training data depends directly on the agent's policy or model. SNI mitigates these effects by selectively applying stochasticity only when useful for regularization, while computing the output deterministically where possible, thereby lowering the variance in gradient estimates and improving the reliability of data collection during training.
2. Information Bottleneck Actor Critic (IBAC): The paper introduces IBAC as a method to produce more robust and generalizable features by applying the Information Bottleneck (IB) principle to the actor-critic framework. IBAC biases RL agents towards more compressed and relevant features by minimizing unnecessary information in the representation, which is particularly beneficial in early training when data is sparse and noisy.

Experimental Validation

The efficacy of these methods is validated through experiments on two RL environments:

• Multiroom Environment: In a procedurally generated grid-world where layouts vary, IBAC-SNI demonstrates substantial improvements in solving complex tasks. Notably, agents trained using IBAC-SNI outperform others in environments with multiple rooms, highlighting its capability to generalize better than conventional RL architectures.
• Coinrun Benchmark: On this complex high-dimensional benchmark, which is recognized for its difficulty in generalization, IBAC-SNI significantly outperforms state-of-the-art methods. The authors show that the combination of weight decay, data augmentation, and IBAC-SNI sets a new standard in achieving higher test performance on unseen levels.

Implications and Speculation

The implications of this paper extend to both practical applications and theoretical understanding:

• Improving RL Agent Deployment: The proposed methods facilitate the deployment of RL agents in real-world scenarios, where robustness to unobserved state variations and generalization to new tasks are pivotal.
• Theoretical Understanding of Generalization: By introducing techniques that explicitly bias feature learning, the paper provides new insights into how RL agents learn transferable skills from limited data, potentially informing future theoretical frameworks on generalization bounds in RL.
• Future Developments in AI: These advancements point towards developing RL systems that not only excel in specific tasks but can adapt to a wide array of applications, enhancing the real-world applicability of autonomous agents.

The paper's contributions lie in addressing fundamental challenges in RL, where the learned policy must efficiently manage exploration and learning without overfitting to specific environments. The introduction of SNI and IBAC represents a pivotal step in developing regularization techniques tailored to RL, encouraging further research on generalization in autonomous systems.