arXiv (All)
open access
Adapting to Changes in Agent Behavior via Finite-Depth Policy Sensitivity
Adapting a reinforcement learning policy to changes in another agent's behavior typically requires a large amount of new interaction data. Policy sensitivity provides a first-order prediction of how a locally optimal policy changes with a behavioral parameter, but its computation requires second-ord
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
- Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation#180710
- Adapting Neural Robot Dynamics on the Fly for Predictive Control#973116
- From World Models to World Action Models: Rethinking Next-State Prediction#1128685
- Locally-Guided Actor-Critic: Training a Goal-conditioned Actor with a Subgoal-aware Critic#799610
- Predicting the Next Response: Demonstrating the Utility of Integrating Artificial Intelligence-Based Reinforcement Learning with Behavior Science.#146034
Record · ID 1216850
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