arXiv (All)
open access
An analysis of Mirror-Descent Soft Actor-Critic
Soft Actor-Critic (SAC) is widely used for entropy-regularised reinforcement learning with continuous action spaces, and practical implementations perform only a few actor steps towards an evolving target. In this work, we prove convergence guarantees when the target policy arises from policy mirror
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
- Soft Adaptive Policy Optimization#459078
- Provably Optimal Reinforcement Learning under Safety Filtering#1034680
- Passivity-Centric Safe Reinforcement Learning for Contact-Rich Robotic Tasks#821687
- SafePG: Safe and Globally Optimal Reinforcement Learning with Hard Constraints#1083552
- Soft $Q(λ)$: A multi-step off-policy method for entropy regularised reinforcement learning using eligibility traces#14071
Record · ID 1130050
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