Deep Reinforcement Learning with Hierarchical Reward Modeling: A Novel Framework for Training Agents

Core Concepts

Exploiting hierarchical structures in feedback signals can enhance reward design and improve agent performance across various tasks.

Abstract

The content introduces HERON, a hierarchical reward modeling framework for reinforcement learning. It addresses challenges in reward design by leveraging hierarchical decision trees based on the importance ranking of feedback signals. HERON outperforms traditional reward engineering methods in traffic light control, code generation, classic control, and robotic control tasks. The framework demonstrates flexibility, robustness to environment changes, and superior performance compared to baselines.
Abstract:

Reward design challenges in RL.
Introduction of HERON framework.
Benefits of utilizing hierarchical structures in feedback signals.
Introduction:

Significance of deep reinforcement learning advancements.
Importance of reward function in benchmark environments.
Challenges in designing rewards for real-world environments.
Method:

Preference elicitation through trajectory comparisons.
Decision tree construction based on feedback signal hierarchy.
Reward learning using labeled dataset D of trajectories.
Experiment - Traffic Light Control:

Comparison with reward engineering baseline and ensemble approaches.
Evaluation of different reward hierarchies' impact on agent behavior.
Experiment - Code Generation:

Performance evaluation on APPS dataset using Pass@K metric.
Generalization testing on MBPP dataset.
More Experiments:

Classic Control experiments with mountain car and pendulum environments.
Robotic Control experiments with Ant, Half-Cheetah, and Hopper tasks.

Stats

"In traffic light control environment [Zhang et al., 2019], where 6 feedback signals have hierarchy: queue length > the average vehicle waiting time > other feedback signals."
"In code generation task [Le et al., 2022] is a sparse reward scenario."

Quotes

"HERON can not only train high performing agents on difficult tasks but also provide additional benefits such as improved sample efficiency and robustness."

Key Insights Distilled From

Deep Reinforcement Learning with Hierarchical Reward Modeling

by Alexander Bu... at arxiv.org 03-22-2024

https://arxiv.org/pdf/2309.02632.pdf

Deeper Inquiries

How does HERON's scale-invariant design contribute to its robustness against changes in the training environment?

HERON's scale-invariant design plays a crucial role in enhancing its robustness against changes in the training environment. This is because HERON's preference elicitation algorithm labels trajectory pairs based on their relative differences, regardless of the absolute scale of the feedback signals. In contrast, traditional reward engineering methods often rely on the magnitude of individual feedback signals to determine weights for combining them into a reward function.
By not depending on the absolute values of feedback signals, HERON can effectively adapt to variations or shifts in these signals without compromising performance. For example, if there is a sudden change in one feedback signal (such as an increase in traffic volume), HERON will still be able to make accurate comparisons between trajectories based on their importance rankings rather than specific numerical values. This ensures that HERON remains stable and effective even when faced with dynamic or evolving training environments.

How might incorporating additional rescaling or margin hyperparameters enhance the preference-based reward model within HERON?

Incorporating additional rescaling or margin hyperparameters can further enhance the preference-based reward model within HERON by providing more control over how preferences are determined and translated into rewards. These enhancements can offer several benefits:

Fine-tuning Reward Shape: By adjusting scaling factors through rescaling parameters like αF(τ) mentioned in the context, practitioners can shape and customize rewards according to specific requirements or domain knowledge. This allows for a more nuanced representation of preferences and priorities within the reward model.

Preference Strength Control: Introducing margin hyperparameters enables modulating how strong preferences are between different trajectories based on their comparison results at each level of hierarchy. This fine-grained control allows for more precise differentiation between preferred and non-preferred trajectories.

Adaptability Across Scenarios: The incorporation of these additional parameters provides flexibility for adapting the preference-based reward model to different scenarios with varying levels of complexity or hierarchy in feedback signals. It allows practitioners to tailor the reward learning process according to specific needs and characteristics of each task.

Overall, by leveraging these supplementary parameters within HERON's framework, users can optimize and refine their approach towards designing hierarchical rewards that align closely with desired outcomes while maintaining robustness across diverse training environments.

What are implications of HERON's flexibility for adapting to different scenarios with varying levels of hierarchy in feedback signals?

The flexibility inherent in HERON's design offers significant advantages when adapting to various scenarios with differing levels of hierarchy among feedback signals:

Customization Based on Domain Knowledge: With flexible ranking mechanisms for determining importance hierarchies among feedback signals, practitioners can easily tailor HERON’s approach according to domain-specific requirements or expert insights.

2 .Scalability Across Tasks: The ability to adjust preferences dynamically enables seamless scalability across tasks where certain aspects may hold greater significance than others.
3 .Robust Performance: The adaptability provided by this flexibility ensures consistent performance even when faced with complex environments where hierarchies may vary significantly.
4 .Efficient Learning Process: By accommodating diverse structures present among feedback signals, such as sparse rewards or multi-objective settings,
-HERONS' flexible nature streamlines learning processes by efficiently capturing key information from varied sources.
5 .Enhanced Generalization Abilities:: The adaptable nature facilitates improved generalization capabilities across tasks by allowing policies trained using
-HERONS' frameworkto effectively transfer knowledge learned from one scenario
-to another despite differencesin hierarchical structuresamongfeedbacksignals
In essence,theflexibilityofHERONServesasakeystrengththatempowerspractitionerswiththeversatilityandadaptabilityneededtosuccessfullyaddressdiversechallengesacrossreinforcementlearningenvironmentsvaryinglevelsofhierarchyinfed-backsignals