Intention-Conditioned Flow Occupancy Models

Chongyi Zheng, Seohong Park, Sergey Levine, Benjamin Eysenbach·June 10, 2025

Summary

InFOM, a latent variable model, significantly enhances reinforcement learning by predicting future states and integrating user intention. It surpasses alternatives, achieving notable improvements in return and success rates across benchmarks. This paper introduces intention-conditioned flow occupancy models for offline unsupervised learning, combining variational inference, successor representations, and expressive flow matching. It discusses recent advancements in reinforcement learning, focusing on techniques like temporal difference flows, one-step diffusion, and unsupervised learning. Key topics include actor-critic methods, self-supervised learning, and latent intentions. The text outlines domain-specific hyperparameters for reinforcement learning, emphasizing fine-tuning and the importance of parameters such as dimensions, learning rates, and reward shaping. It presents success rates under different settings for InFOM, showcasing its effectiveness.

Background
Reinforcement Learning Overview
Core Concepts
Agents, environments, rewards, policies
Learning through trial and error
Traditional Challenges
State Space Complexity
High-dimensional state spaces
Exploration vs. exploitation
Recent Advances
Model-based Approaches
Predictive models for state transitions
Integration of user intention
Objective
Motivation for InFOM
Enhancing prediction accuracy
Improving return and success rates
Research Aim
Developing a latent variable model for reinforcement learning
Integrating user intention for better decision-making
Method
Intention-Conditioned Flow Occupancy Models
Model Architecture
Variational Inference
Learning latent variables
Posterior approximation
Successor Representations
Predicting future state values
Incorporating temporal dynamics
Expressive Flow Matching
Matching distributions for improved predictions
Data Handling
Offline Learning
Utilizing historical data
Unsupervised learning
Hyperparameter Tuning
Domain-specific settings
Optimization of dimensions, learning rates, reward shaping
Recent Reinforcement Learning Techniques
Temporal Difference Flows
Learning from temporal differences
Flow-based models for prediction
One-Step Diffusion
Incremental learning updates
Efficient model adaptation
Unsupervised Learning in RL
Learning without explicit supervision
Enhancing generalization
Key Topics
Actor-Critic Methods
Policy gradient techniques
Value function approximation
Self-Supervised Learning
Learning from internal states
Enhancing exploration
Latent Intentions
Incorporating hidden user goals
Improving decision-making
Case Study: InFOM
Implementation Details
Domain-Specific Hyperparameters
Fine-tuning for optimal performance
Parameter selection for different environments
Success Rates
Benchmark comparisons
InFOM's performance metrics
Results and Analysis
Return and Success Rates
Quantitative improvements over alternatives
Detailed analysis of InFOM's effectiveness
Case Studies
Real-world applications and outcomes
Insights into model behavior
Conclusion
Future Directions
Enhancements to InFOM
Advanced latent variable modeling
Improved integration of user intentions
Integration with Other Techniques
Combining InFOM with deep learning
Expanding to multi-agent systems
Summary
Key Takeaways
The significance of InFOM in reinforcement learning
The role of latent intentions in decision-making
The impact of hyperparameter tuning on model performance
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
How does InFOM leverage latent intentions to improve performance in reinforcement learning, and what recent advancements in the field does it build upon?
What domain-specific hyperparameters are crucial for fine-tuning InFOM in reinforcement learning tasks, and why are they important?
How does InFOM integrate variational inference, successor representations, and flow matching for offline unsupervised learning?
What are the key innovations of the InFOM model in the context of reinforcement learning?