Zero-Shot Generalization of Vision-Based RL Without Data Augmentation (2410.07441v1)

Abstract: Generalizing vision-based reinforcement learning (RL) agents to novel environments remains a difficult and open challenge. Current trends are to collect large-scale datasets or use data augmentation techniques to prevent overfitting and improve downstream generalization. However, the computational and data collection costs increase exponentially with the number of task variations and can destabilize the already difficult task of training RL agents. In this work, we take inspiration from recent advances in computational neuroscience and propose a model, Associative Latent DisentAnglement (ALDA), that builds on standard off-policy RL towards zero-shot generalization. Specifically, we revisit the role of latent disentanglement in RL and show how combining it with a model of associative memory achieves zero-shot generalization on difficult task variations without relying on data augmentation. Finally, we formally show that data augmentation techniques are a form of weak disentanglement and discuss the implications of this insight.

• The paper introduces ALDA, which leverages a disentangled latent space and associative memory to enable zero-shot generalization in vision-based RL without relying on data augmentation.
• The method integrates off-policy RL with a quantized latent autoencoder, showing strong performance on challenging distribution shifts like 'color hard' and DistractingCS environments.
• The study demonstrates that achieving strong disentanglement in latent representations can replace data augmentation, leading to robust and efficient generalization in unseen tasks.

Overview of Zero-Shot Generalization in Vision-Based Reinforcement Learning

This essay provides an in-depth analysis of the paper which investigates zero-shot generalization in vision-based reinforcement learning (RL) systems without the use of data augmentation. The authors propose a novel method, Associative Latent Disentanglement (ALDA), which leverages disentangled representation learning combined with associative memory mechanisms to achieve zero-shot generalization.

Key Contributions

1. Proposal of ALDA

The cornerstone of the paper is the introduction of the ALDA model. ALDA integrates off-policy RL with a disentangled representation framework and an associative memory model. This integration enables zero-shot generalization across challenging tasks in vision-based RL without conventional data augmentation methodologies. The model utilizes a disentangled latent space that aids in efficiently mapping out-of-distribution (OOD) observations back to known data distributions.

2. Disentangled Representation and Associative Memory

The authors draw inspiration from computational neuroscience to propose that associative memory mechanisms can facilitate OOD generalization. ALDA employs the QLAE (Quantized Latent Autoencoder) method to learn disentangled representations. The latent space of QLAE is augmented with mechanisms analogous to modern Hopfield networks, enabling task-relevant associations when encountering OOD data.

3. Evaluation on Generalization Benchmarks

Extensive experiments are conducted on vision-based RL tasks from the DeepMind Control Suite. The model's performance is evaluated on challenging distribution shifts, notably "color hard" and "DistractingCS" environments. The experiments consistently demonstrate that ALDA outperforms or matches other state-of-the-art approaches like SVEA and DARLA without leveraging additional data augmentation.

4. Weak vs. Strong Disentanglement

An insightful theoretical contribution is the formal demonstration that data augmentation can be seen as creating "weak" disentanglement within the latent space. The authors argue that strong disentanglement, as facilitated by their model, leads to better generalization without the extensive computational costs associated with data augmentation.

Analysis of Results

Performance Evaluation: On vision-based RL tasks, ALDA surpassed most prior techniques in achieving high performance in unseen environments. However, it is noted that a performance gap exists in scenarios with extreme environmental perturbations.

Latent Space Dynamics: The latent space has been shown to separate task-relevant from task-irrelevant information effectively, providing a robust framework to handle complex observations. The empirical evidence suggests successful disentanglement of task-specific factors, facilitating the zero-shot generalization observed.

Implications and Future Direction

The implications of ALDA are significant in both theoretical and practical RL applications. Theoretically, it introduces a paradigm where data augmentation is not a necessity for OOD generalization, providing a computationally cheaper and potentially more stable solution. Practically, it paves the way for deploying RL systems in dynamic and unforeseen real-world scenarios, improving the adaptability of learned models.

Future Research Directions: The promising results of ALDA indicate several avenues for further exploration:

• Extending the model to explicitly handle temporal aspects in decision-making tasks.
• Exploring more sophisticated Hopfield network variants for associative memory.
• Investigating the potential to dynamically adjust the dimensionality of the latent space as per task complexity.

Overall, this paper presents a substantial advancement in the field of reinforcement learning by eliminating the dependence on massive datasets and data augmentation. While further research is required to refine and extend the applicability of the proposed methods, ALDA represents a pivotal step towards RL agents capable of human-like adaptability and generalization.