HIQL: Offline Goal-Conditioned Reinforcement Learning with Latent States as Actions

Główne pojęcia

The author proposes HIQL, a hierarchical algorithm for offline goal-conditioned RL that leverages a single value function to drive the learning of low-level and high-level policies. This approach simplifies the method while achieving strong performance in various challenging tasks.

Streszczenie

HIQL introduces a hierarchical algorithm for offline goal-conditioned RL, leveraging a single value function for policy learning. The method addresses challenges in estimating value functions accurately for distant goals and demonstrates robustness to noise through hierarchical decomposition. HIQL outperforms previous methods on state-based and pixel-based benchmarks, showcasing its scalability and effectiveness. The ability to leverage action-free data and incorporate representation learning further enhances HIQL's performance across various environments.
Key points:

Unsupervised pre-training is crucial in reinforcement learning.
Goal-conditioned RL offers self-supervised learning from unlabeled data.
HIQL proposes a hierarchical approach using one value function for policy extraction.
The method shows robustness to noisy value functions and achieves superior performance on diverse benchmarks.
Leveraging action-free data and incorporating representations contribute to HIQL's success.

Statystyki

In reinforcement learning (RL), goal-conditioned RL can provide an analogous self-supervised approach for making use of large quantities of unlabeled (reward-free) data.
Based on the idea that reaching distant goals entails passing through closer subgoals, a hierarchical algorithm is proposed for goal-conditioned RL from offline data.
Through experiments on six types of state-based and pixel-based offline goal-conditioned RL benchmarks, HIQL significantly outperforms previous methods.

Cytaty

"Our code is available at https://seohong.me/projects/hiql/"

Kluczowe wnioski z

HIQL

by Seohong Park... o arxiv.org 03-12-2024

https://arxiv.org/pdf/2307.11949.pdf

Głębsze pytania

How does HIQL compare to other hierarchical RL methods

HIQL stands out from other hierarchical RL methods by offering a simple yet effective approach to offline goal-conditioned RL. Unlike many complex hierarchical methods that involve multiple components and objectives, HIQL streamlines the process by utilizing a single value function to drive the learning of both low-level and high-level policies as well as representations. This streamlined approach not only simplifies the training process but also allows for clearer learning signals for policy extraction, making it more robust to errors in the learned value function.

What are the implications of using action-free data in reinforcement learning

Using action-free data in reinforcement learning has significant implications for leveraging previously collected and passively observed data without reward labels or task information. By incorporating state-only trajectories into value learning, algorithms like HIQL can learn optimal goal-conditioned policies even with limited action information. This ability to make use of passive data accelerates learning processes and enables models to generalize better across tasks by focusing on long-term directions toward goals rather than detailed layout specifics.

How can the concept of latent states as actions be applied in other machine learning domains

The concept of latent states as actions can be applied in various machine learning domains beyond reinforcement learning. For instance:

In unsupervised pre-training for computer vision or natural language processing, latent states could serve as intermediate representations that capture essential features within the data.
In generative modeling such as variational autoencoders (VAEs) or generative adversarial networks (GANs), latent states could represent underlying factors influencing generated outputs.
In sequential decision-making tasks like recommendation systems or time series forecasting, latent states could encode relevant context information that guides future predictions or recommendations.
By incorporating latent states as actions in these domains, models can benefit from enhanced representation capabilities and improved performance on complex tasks requiring multi-step reasoning or long-horizon planning.