arXiv (All)
open access
Finite-Time Guarantees for Constrained Reinforcement Learning under Partial Observability
We study reinforcement learning in constrained partially observable Markov decision processes (CPOMDPs), where an agent maximizes reward subject to a cost constraint using only incomplete information about the underlying state. The lack of state observability may require optimal policies to depend o
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
- Provably Optimal Reinforcement Learning under Safety Filtering#1034680
- Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective#665755
- Revisiting Reinforcement Learning with Verifiable Rewards from a Contrastive Perspective#664255
- On the Complexity Gap Between POMDPs and Multi-Environment POMDPs#1325906
- Don't Mix Rewards, Mix Policies: Policy Decomposition and Optimization for Multi-Reward RL#422305
Record · ID 1356884
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