arXiv (All)
open access
Independent Reinforcement Learning in Discounted Markov Games
artificial-intelligencecomputer-science-and-game-theorymachine-learningoptimization-and-controlsystems-and-control
In this work, we study radically uncoupled learning in discounted general-sum Markov games. Assuming ``$\mathsf{ETH}$ for $\mathsf{PPAD}$", we show that, for every fixed discount factor, there is no polynomial-time algorithm for computing inverse-polynomially accurate coarse correlated equilibria in
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
- Markovian Nonconvex ADMM for Reinforcement Learning: Bellman-Resolvent Stability Beyond Smooth Blocks#1132060
- Sharp Statistical Rates for Asynchronous TD Learning with Markovian Data#1143481
- Aspiration-based Perturbed Learning Automata in Weakly-Acyclic Games with Noisy Utility Measurements#793519
- Aspiration-based Perturbed Learning Automata in Weakly-Acyclic Games with Noisy Utility Measurements#789515
- Projection-Free Algorithms for Nonsmooth Stochastic Convex-Concave Saddle-Point Problems#1129869
Record · ID 819948
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