arXiv (All)
open access
Diffusion-Augmented Markov Decision Processes for Maximum Entropy Reinforcement Learning
Diffusion models provide an expressive framework for sampling from complex, unnormalized distributions. In this work, we extend Maximum Entropy Reinforcement Learning (ME-RL) to diffusion-based policies by introducing Diffusion-Augmented Markov Decision Processes (DA-MDPs). DA-MDPs interpret each re
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL#422305
- Official Review · ICLR 2026#787023
- Thompson Sampling for Infinite-Horizon Discounted Decision Processes#1049572
- Multitask Reinforcement Learning for Assisting Choice Model Specification#1012627
- Inversely Learning Transferable Rewards via Abstracted States#1115470
Record · ID 1142590
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive