arXiv (All)
open access
Direction-Conditioned Policies for Online Goal-Conditioned Reinforcement Learning
Contrastive Reinforcement Learning (CRL) learns representations that estimate goal reachability, yet its policy remains conditioned on raw goals and therefore does not directly exploit the geometry encoded by its critic. We introduce Direction-Conditioned Policies (DCP), a method built around a smal
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
- DAGR: State-Conditioned Goal Representations via Difference-Aware Goal Cross-Attention#1187488
- Balancing Risk and Reward : A General Architecture for Safe Exploration in Robotic Learning#1066030
- Do Better Goal Representations Improve Goal-Conditioned Reinforcement Learning?#1145068
- ArenaFlow: From Trajectory Ranking to Hierarchical Credit Propagation for Open-Ended Agent RL#1035782
- Consolidating RLVR Capabilities Across Domains: A Deep Dive into Fusion Paradigms#1035607
Record · ID 1201100
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