Contrastive Abstraction for Reinforcement Learning (2410.00704v1)

Abstract: Learning agents with reinforcement learning is difficult when dealing with long trajectories that involve a large number of states. To address these learning problems effectively, the number of states can be reduced by abstract representations that cluster states. In principle, deep reinforcement learning can find abstract states, but end-to-end learning is unstable. We propose contrastive abstraction learning to find abstract states, where we assume that successive states in a trajectory belong to the same abstract state. Such abstract states may be basic locations, achieved subgoals, inventory, or health conditions. Contrastive abstraction learning first constructs clusters of state representations by contrastive learning and then applies modern Hopfield networks to determine the abstract states. The first phase of contrastive abstraction learning is self-supervised learning, where contrastive learning forces states with sequential proximity to have similar representations. The second phase uses modern Hopfield networks to map similar state representations to the same fixed point, i.e.\ to an abstract state. The level of abstraction can be adjusted by determining the number of fixed points of the modern Hopfield network. Furthermore, \textit{contrastive abstraction learning} does not require rewards and facilitates efficient reinforcement learning for a wide range of downstream tasks. Our experiments demonstrate the effectiveness of contrastive abstraction learning for reinforcement learning.

• The paper introduces a contrastive abstraction method that leverages contrastive learning and modern Hopfield networks to cluster and refine state representations for improved RL stability.
• It incorporates a dynamic abstraction control mechanism by learning the optimal temperature parameter to balance state resolution and computational efficiency.
• Experimental results in environments like Maze2D, CIFAR, and Minecraft demonstrate robust policy learning and scalability of the proposed approach.

Contrastive Abstraction for Reinforcement Learning: A Summary

The paper "Contrastive Abstraction for Reinforcement Learning" by Vihang Patil et al. addresses a notable challenge in reinforcement learning (RL): the inherent difficulty in learning effective policies when dealing with substantial state spaces and long trajectories. This complexity often results in unstable end-to-end learning in deep reinforcement learning (DRL). The authors propose a methodology they term "contrastive abstraction learning" to mitigate this challenge by clustering states into abstract states using contrastive learning, followed by refinement using modern Hopfield networks (MHNs).

Key Contributions

1. Contrastive Abstraction Learning:

The proposed method comprises two primary phases. Initially, contrastive learning is employed to group states with sequential proximity into similar representations. This phase is self-supervised and utilizes the InfoNCE objective to ensure representations of sequentially close states are similar. Following this, MHNs are used to map similar representations to fixed points, which serve as abstract states. The number of abstract states can be adjusted through the MHN's temperature parameter, offering control over abstraction granularity.

1. Controlling Abstraction Level:

The paper introduces a mechanism to dynamically control the level of abstraction via a learned temperature parameter of the MHN. This is accomplished by training a network to adjust this parameter, ensuring the abstraction level is contextually suitable for various tasks. This adaptive abstraction is key to balancing the trade-off between state space resolution and computational efficiency.

1. Practical Applications and Demonstrations:

The efficacy and applicability of contrastive abstraction learning are demonstrated across diverse environments such as CIFAR images, Maze2D, and MiniGrid, as well as a complex domain like Minecraft. The authors show how the abstract states facilitate RL by reducing the state space complexity, thereby enabling efficient learning of policies.

Methodology

Contrastive Learning

Contrastive learning forms the basis of the initial abstraction by ensuring that states within short temporal distances in trajectories yield similar representations. Positive state pairs are chosen based on sequential proximity, while negative pairs include states far apart in time or from different trajectories. This is formalized using the InfoNCE objective, which effectively clusters the state representations.

Modern Hopfield Networks

MHNs further refine these state clusters by mapping them to fixed points, defined as abstract states. The degree of abstraction, regulated by the temperature parameter of the MHN, allows transitions between coarse to fine abstraction levels. The paper includes a detailed theoretical rationale for using MHNs, emphasizing their stability and high capacity for pattern storage.

Dynamic Abstraction Control

A notable innovation in this work is the introduction of a control mechanism for the level of abstraction. This is realized by learning a function that predicts the optimal temperature parameter for the MHNs based on the current state representation. This adaptiveness is crucial, allowing the abstraction to be fine-tuned dynamically for different segments of the state space or different tasks within the same environment.

Experimental Results

The experimental validation demonstrates robust performance across various challenging environments:

• CifarEnv: The method accurately identifies ten distinct classes of images from the CIFAR-100 dataset, clustering states into well-defined abstract states that correspond to the image classes.
• Maze2D: Experiments highlight how the method simplifies navigation tasks by abstracting the complex maze state space into key regions.
• RedBlueDoor (MiniGrid): The abstract states represent critical positions and states within the environment, such as rooms and door states, significantly easing the policy learning process.
• Minecraft: Application to this complex, partially observable environment shows that contrastive abstraction learning can handle high-dimensional state spaces effectively.

Implications and Future Directions

The implications of this research are substantial for both theoretical and practical advancements in RL. By providing a robust framework for state abstraction, the proposed method enhances RL efficiency and stability. Practically, this can lead to solutions that are computationally feasible for real-world applications with extensive state spaces.

Future research could further explore the integration of contrastive abstraction learning with various RL algorithms, extending its capability to more complex environments and tasks. Another intriguing direction would be enhancing the control mechanism, potentially incorporating more sophisticated neural network architectures or learning paradigms.

Conclusion

This paper provides a comprehensive approach to state abstraction in RL through contrastive abstraction learning. The combination of contrastive learning and MHNs offers a scalable and effective means to manage large state spaces, paving the way for more stable and efficient RL algorithms. The results demonstrate its versatility and robustness, indicating significant potential for further developments in the field.