arXiv (All)
open access
Advanced Policies: A First-Principles Path from Policy Gradient to Q-Learning
We apply entropy to reinforcement learning in two ways: entropy-augmented reward defines the soft objective, while relative-entropy regularization controls the size of a policy-improvement step without changing that objective. Together they yield a one-parameter family of advanced policies connectin
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
- Q-learning#7267
- End-to-end QP-based policies: A unified perspective on robust control and robot learning#1115861
- Noisy-Space Policy Gradient for Diffusion Policies in Offline Reinforcement Learning#974961
- On BatchNorm Forward Modes in Value-Based Reinforcement Learning#974000
- Soft Fitted Q-Iteration without Bellman Completeness: Occupancy Reweighting and Temperature Annealing#798711
Record · ID 1117185
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