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Bellman Diffusion Models

generative-ai › diffusion

📄 Abstract

Abstract: Diffusion models have seen tremendous success as generative architectures. Recently, they have been shown to be effective at modelling policies for offline reinforcement learning and imitation learning. We explore using diffusion as a model class for the successor state measure (SSM) of a policy. We find that enforcing the Bellman flow constraints leads to a simple Bellman update on the diffusion step distribution.

Authors (2)

Liam Schramm

Abdeslam Boularias

Submitted

July 16, 2024

arXiv Category

cs.LG

arXiv PDF

Key Contributions

This paper explores using diffusion models to represent the successor state measure (SSM) for policies in offline reinforcement learning and imitation learning. By enforcing Bellman flow constraints, they derive a simple Bellman update on the diffusion step distribution, integrating generative power with RL principles.

Business Value

Could lead to more robust and capable AI agents in domains like robotics and autonomous systems by improving how policies are learned and represented, especially from offline data.

Paper Metadata

Innovation Type

Methodological

Deployment Feasibility

Moderate. Diffusion models can be computationally intensive, but their application to policy learning is promising.

Limitations Addressed

Modeling complex state-action distributions for policies,Effectively applying generative models to RL tasks

Performance Gains

Potentially enables more effective policy learning in offline RL by leveraging the expressive power of diffusion models.

Technical Tags

diffusion modelsBellman equationsuccessor state measure (SSM)offline reinforcement learningimitation learninggenerative architecturespolicy modelingBellman flow constraintsdiffusion update

Research Topics

Generative ModelsReinforcement LearningDeep Learning ArchitecturesSequential Decision Making

Methods & Architectures

Bellman flow constraintsdiffusion model formulation for SSM Diffusion Models

Applications & Tasks

Robotics Autonomous Systems Game Playing Reinforcement Learning Modeling policies for offline RLImproving generative capabilities for sequential data Learning policies using diffusion modelsGenerating successor states

Related Fields

Machine LearningDeep LearningReinforcement LearningComputer VisionGenerative Models

Keywords

Diffusion ModelsReinforcement LearningOffline RLImitation LearningSuccessor State MeasureBellman EquationGenerative AIPolicy LearningDeep LearningSequential Decision Making

Academic Context

#Generative Models#Reinforcement Learning#Deep Learning Architectures#Sequential Decision Making

Commercial Potential

Potential Products

Advanced RL training frameworksGenerative policy models for robotics

Target Industries

RoboticsAutonomous VehiclesGamingAI Research

Use Case Examples

Learning complex manipulation policies for robots from demonstration dataDeveloping sophisticated game-playing agentsGenerating realistic trajectories for autonomous systems

Competitive Edge

Integrates the strengths of diffusion models (generative power) with RL requirements (policy learning) through a novel application of Bellman constraints.

Market Opportunity

Rapid growth in generative AI and its applications in robotics and autonomous systems.

Revenue Models

Licensing of advanced generative policy modelsspecialized AI training services.

Resource Requirements

Compute Needs

High (for training diffusion models)

Data Requirements

Requires datasets suitable for offline RL or imitation learning.

Deployment Constraints

Computational cost of diffusion models can be a limitation for real-time applications.

Scalability

Scalability depends on the efficiency of the diffusion model implementation and the complexity of the task.

Production Readiness

Maturity Level

Research

Time to Market

2-4 years

Patent Potential

Moderate

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