Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning: Enhancing Expert-Quality Data Generation

Keskeiset käsitteet

Guided Data Augmentation (GuDA) enhances offline RL by generating expert-quality augmented data from suboptimal experience.

Tiivistelmä

In the realm of offline reinforcement learning, the challenge lies in learning from a fixed dataset without task interaction. GuDA introduces a human-guided data augmentation framework to create expert-quality augmented data. By leveraging user-defined sampling procedures, GuDA enables the generation of data that represents progress towards task completion. This approach shifts the burden from demonstrating optimal actions to understanding when augmented data signifies task advancement. Empirical evaluations across various tasks demonstrate GuDA's superiority over random and model-based DA strategies, showcasing its effectiveness in enabling learning from limited and potentially suboptimal data.

Tilastot

In maze2d-medium, GuDA yields returns 3x larger than other strategies.
GuDA outperforms MoCoDA in antmaze-large with significance.
BC achieves larger returns with GuDA compared to Random DA or MoCoDA.

Lainaukset

"We propose Guided Data Augmentation (GuDA), a human-guided DA framework capable of generating large amounts of expert-quality data."
"GuDA enables practitioners to generate expert data from potentially suboptimal experience without the expense of task interaction."
"Empirically, GuDA enables agents to learn effective policies given a small amount of data – even highly suboptimal data."

Tärkeimmät oivallukset

Guided Data Augmentation for Offline Reinforcement Learning and Imitation Learning

by Nicholas E. ... klo arxiv.org 03-19-2024

https://arxiv.org/pdf/2310.18247.pdf

Syvällisempiä Kysymyksiä

How can Guided Data Augmentation be adapted for online reinforcement learning scenarios

In online reinforcement learning scenarios, Guided Data Augmentation (GuDA) can be adapted by incorporating real-time feedback from the environment or an expert. Instead of relying solely on a static dataset, GuDA in online RL can dynamically generate augmented data based on the agent's interactions with the environment. This adaptive approach allows GuDA to continuously improve the quality and coverage of the training data as the agent learns.
By leveraging real-time feedback, GuDA can adjust its sampling procedures based on the current state of the agent and its progress towards task completion. For example, if an agent consistently fails at a particular task, GuDA can focus on generating augmented data that addresses those specific failure modes. Additionally, incorporating expert demonstrations during training can provide valuable insights into how to guide data augmentation effectively in dynamic environments.

What are the implications of relying on domain knowledge for specifying sampling procedures in GuDA

Relying on domain knowledge for specifying sampling procedures in Guided Data Augmentation (GuDA) has both advantages and implications.
Advantages:

Task-Specific Expertise: Domain knowledge allows for tailored sampling procedures that align with the intricacies of each task.
Efficient Data Generation: By understanding what constitutes progress towards task completion, users can efficiently guide data augmentation without needing to specify exact action sequences.
Improved Performance: Domain-specific rules ensure that augmented data closely resembles expert behavior, leading to more effective policy learning.

Implications:

Expertise Requirement: Users need a deep understanding of the task dynamics to define effective sampling procedures accurately.
Subjectivity: The effectiveness of GuDA may vary depending on individual interpretations of what constitutes progress in a given task.
Complexity: Designing appropriate sampling procedures for complex tasks may require significant time and effort from domain experts.

Overall, while relying on domain knowledge enhances GuDA's performance by tailoring augmentations to specific tasks' requirements, it also introduces challenges related to expertise and subjectivity.

How might incorporating expert feedback impact the performance of GuDA in real-world applications

Incorporating expert feedback into Guided Data Augmentation (GuDA) could significantly impact its performance in real-world applications:

Enhanced Dataset Quality: Expert feedback ensures that augmented data generated by GuDA closely aligns with optimal strategies or behaviors demonstrated by experts.

Faster Learning: By providing guidance based on expert demonstrations or insights into successful strategies, GuDa could accelerate policy learning processes.

3.. 4Robustness: Incorporating expert feedback helps address potential biases or limitations present in suboptimal initial datasets used for offline RL tasks
However,
Onthe other hand,
over-reliance
onexpertfeedback
may introducebiasesorlimitthe diversityofaugmenteddatageneratedbyGuDAbasedonaparticularindividual'sperspectiveorapproachtothetaskat hand.Itisimportanttomaintainabalancebetweenutilizingexpertinsightsandensuringdiversityinthesampledproceduresfora morecomprehensivelearningexperienceinreal-worldapplications.
Overall,includingexpertfeedbackcanpositivelyimpacttheperformanceofGuidedDataAugmentationbyenhancingthedatasetquality,facilitatingfasterlearning,andimprovingtherobustnessoflearnedpolicies.However,carefulconsiderationmustbegiventomaintaina balancebetweentheuseofexpertsuggestionsandtheneedfordiversityinthegeneratedaugmenteddataformaximumeffectivenessandreliabilityinreal-worldscenarios.