arXiv (All)
open access
GR2PO: Group Relative Return Policy Optimization for Continuous Robot Control
Actor-critic architecture has been widely used in continuous robot control. However, they rely on learning a value network, introducing additional computational overhead during training. Moreover, policy learning may also be affected by the approximation error of value estimation. Critic-free group
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
- SALSA-RL: Stability Analysis in the Latent Space of Actions for Reinforcement Learning#128348
- CALOS: Control-Affine Lyapunov On-manifold Safety Layer for Safe Deep Reinforcement Learning for Quadrotors#1011481
- Multiple Task Optimization with a Mixture of Controllers for Motion Generation#779170
- Policy Gradient Methods for Non-Markovian Reinforcement Learning#175266
- Condensing AI-based attitude control using kolmogorov–arnold networks for memory efficiency / Kirill Djebko, Patrick Schurk, Tom Baumann, Frank Puppe, Sergio Montenegro#1106428
Record · ID 1014898
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