arXiv (All)
open access
Environment Parameter Gradient Theorem for Co-Design in Reinforcement Learning
Reinforcement learning (RL) is traditionally concerned with learning a control policy for a fixed environment. In many engineering systems, however, the environment itself is alterable, i.e., physical or operational parameters can be tuned to shape the system's transition dynamics and costs experien
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
- SINDy-RL for interpretable and efficient model-based reinforcement learning#248603
- HiRE: Hindsight Reward Editing for Policy Finetuning#1063279
- ReST-RL: Reinforcing LLM Reasoning through Unified Self-Training and Value-Guided Search#984067
- Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning#925165
- Theoretical Foundations and Effective Algorithms for Policy-Aware Simulator Learning#920930
Record · ID 973269
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