arXiv (All)
open access
Energy-Based Transfer for Reinforcement Learning
Reinforcement learning remains sample inefficient, particularly when agents must adapt over time to related but distinct tasks, as in continual learning settings. Efficiency can be improved by transferring knowledge from a pretrained teacher policy to guide exploration in new but related tasks. Howe
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
- RL Starts before RL: On Policy Distillation for Better Reinforcement Learning#1064689
- Energy optimized scheduling in wireless sensor networks (WSNs) using hybrid bio-inspired reinforcement learning approach.#125610
- A Good Self-Teacher Meets the Student Where They Are: Joint On-Policy Learning and Teaching#1234872
- A Good Self-Teacher Meets the Student Where They Are: Joint On-Policy Learning and Teaching#1227323
- K2P: Label-Free Knowledge to Prompt Distillation#1143499
Record · ID 1412726
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