Learning Action-based Representations Using Invariance

Core Concepts

Action-bisimulation encoding improves representation learning for reinforcement agents by capturing multi-step controllability.

Abstract

Reinforcement learning agents need to identify relevant state features amidst distractors. Representation learning filters out irrelevant features, improving sample efficiency. Action-bisimulation extends single-step controllability with recursive invariance. Pretraining on action-bisimulation enhances sample efficiency in various environments. The method captures long-term controllability and control-relevant state features effectively. Empirical results show the superiority of action-bisimulation over other representation methods. Background distractors challenge traditional methods but have minimal impact on action-bisimulation. The encoding captures multi-step relationships and is robust to uncontrollable distractors.

Stats

Myopic controllability captures the moment before a crash but not distant control relevance. Action-bisimulation extends single-step controllability with recursive invariance constraint.

Quotes

Key Insights Distilled From

Learning Action-based Representations Using Invariance

by Max Rudolph,... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16369.pdf

Deeper Inquiries

How does action-bisimulation compare to other unsupervised representation learning methods

Action-bisimulation stands out from other unsupervised representation learning methods by focusing on capturing controllability in the state space. While methods like beta-VAE and CURL rely on reconstruction or contrastive objectives, action-bisimulation leverages a novel invariant metric to learn multi-step control-based representations. This approach extends single-step controllability with a recursive invariance constraint, allowing it to smoothly discount distant state features that are relevant for control over long horizons. In comparison, beta-VAE may struggle with capturing fine-grained changes such as agent movements, while CURL might not be robust to background distractors.

What are the potential limitations of relying on myopic representations for long-term decision-making

Relying solely on myopic representations for long-term decision-making can have limitations when facing complex environments with sparse rewards and multiple irrelevant features. Myopic representations capture only immediate information before an event occurs but may fail to consider the broader context or anticipate future consequences of actions. This limitation can lead to suboptimal decision-making strategies and hinder the agent's ability to navigate efficiently through environments requiring long-horizon planning.

How can the concept of bisimulation be applied in other areas beyond reinforcement learning

The concept of bisimulation can be applied beyond reinforcement learning in various areas where understanding equivalence relations between states is crucial. For example: Formal Verification: Bisimulation is used in formal verification techniques for verifying system correctness by comparing different models' behaviors. Distributed Systems: In distributed systems, bisimulation can help analyze concurrent processes' behavior and ensure consistency across nodes. Cryptography: Bisimulations are utilized in cryptographic protocols to verify security properties and establish equivalence between different encryption schemes. Software Engineering: Bisimilarities are employed in software engineering for model checking, testing equivalence of programs under certain conditions. By applying bisimulation concepts outside of reinforcement learning, we can enhance system analysis, verification processes, security protocols, and software development practices through rigorous equivalence comparisons between different entities or systems based on their behaviors or characteristics.