MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference
arXiv:2602.15206v1 Announce Type: new Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly learn reward functions from heterogeneous feedback types such as demonstrations, comparisons, ratings, and stops that provide qualitatively different signals. We address this challenge by formulating reward learning from multiple feedback types as Bayesian inference over a shared latent reward function, where each feedback type contributes information through an explicit likelihood. We introduce a scalable amortized variational inference approach that learns a shared reward encoder and feedback-specific likelihood decoders and is trained by optimizing a single evidence lower bound. Our approach avoids reducing feedback to a common intermediate representation and eliminates the need for manual loss balancing. Across discrete and continuous-control benchmarks, we sho
arXiv:2602.15206v1 Announce Type: new Abstract: Reward learning typically relies on a single feedback type or combines multiple feedback types using manually weighted loss terms. Currently, it remains unclear how to jointly learn reward functions from heterogeneous feedback types such as demonstrations, comparisons, ratings, and stops that provide qualitatively different signals. We address this challenge by formulating reward learning from multiple feedback types as Bayesian inference over a shared latent reward function, where each feedback type contributes information through an explicit likelihood. We introduce a scalable amortized variational inference approach that learns a shared reward encoder and feedback-specific likelihood decoders and is trained by optimizing a single evidence lower bound. Our approach avoids reducing feedback to a common intermediate representation and eliminates the need for manual loss balancing. Across discrete and continuous-control benchmarks, we show that jointly inferred reward posteriors outperform single-type baselines, exploit complementary information across feedback types, and yield policies that are more robust to environment perturbations. The inferred reward uncertainty further provides interpretable signals for analyzing model confidence and consistency across feedback types.
Executive Summary
The article titled 'MAVRL: Learning Reward Functions from Multiple Feedback Types with Amortized Variational Inference' introduces a novel approach to reward learning in reinforcement learning (RL) by leveraging multiple heterogeneous feedback types. The authors propose a Bayesian inference framework that integrates demonstrations, comparisons, ratings, and stops to infer a shared latent reward function. This method employs amortized variational inference to learn a shared reward encoder and feedback-specific likelihood decoders, eliminating the need for manual loss balancing. The study demonstrates that this approach outperforms single-type baselines, exploits complementary information across feedback types, and yields policies robust to environmental perturbations. The inferred reward uncertainty also provides interpretable signals for analyzing model confidence and consistency.
Key Points
- ▸ Introduction of a Bayesian inference framework for reward learning from multiple feedback types.
- ▸ Use of amortized variational inference to learn a shared reward encoder and feedback-specific decoders.
- ▸ Elimination of the need for manual loss balancing.
- ▸ Demonstration of superior performance over single-type baselines.
- ▸ Provides interpretable signals for model confidence and consistency analysis.
Merits
Innovative Framework
The proposed Bayesian inference framework is innovative as it integrates multiple heterogeneous feedback types without reducing them to a common intermediate representation. This approach leverages the unique information provided by each feedback type, enhancing the robustness and accuracy of the learned reward function.
Scalability
The use of amortized variational inference makes the approach scalable, allowing it to handle large datasets and complex environments efficiently. This scalability is crucial for real-world applications where large-scale data is common.
Robustness
The inferred reward uncertainty provides a measure of model confidence and consistency, which is valuable for interpreting the reliability of the learned policies. This feature enhances the robustness of the approach in practical applications.
Demerits
Complexity
The complexity of the proposed framework may pose challenges in implementation and understanding. The integration of multiple feedback types and the use of Bayesian inference require a deep understanding of both machine learning and statistical methods, which may limit its accessibility to practitioners.
Computational Resources
The approach may require significant computational resources for training, especially when dealing with large datasets and complex environments. This could be a limitation for applications with constrained computational budgets.
Generalization
While the study demonstrates superior performance on benchmarks, the generalization of the approach to real-world, dynamic environments remains to be thoroughly validated. Further research is needed to ensure its effectiveness in diverse and unpredictable scenarios.
Expert Commentary
The article presents a significant advancement in the field of reinforcement learning by addressing the challenge of learning reward functions from multiple feedback types. The proposed Bayesian inference framework, combined with amortized variational inference, offers a robust and scalable solution that leverages the unique information provided by each feedback type. The elimination of manual loss balancing is a notable improvement over existing methods, as it simplifies the learning process and enhances the model's ability to exploit complementary information across feedback types. The study's demonstration of superior performance on benchmarks, along with the provision of interpretable signals for model confidence and consistency, underscores the practical value of the approach. However, the complexity of the framework and the potential computational requirements may pose challenges for widespread adoption. Further research is needed to validate the approach's generalization to real-world, dynamic environments and to explore its potential applications in various domains. Overall, the article makes a substantial contribution to the field and sets the stage for future advancements in reward learning and reinforcement learning.
Recommendations
- ✓ Further validation of the proposed approach in real-world, dynamic environments to ensure its robustness and generalization.
- ✓ Exploration of methods to simplify the implementation and reduce the computational requirements of the framework to enhance its accessibility and practical applicability.
