Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning
No confident code implementation found for this paper. 5 possible matches can be shown below.
No confident code implementation found for this paper. 5 possible matches can be shown below.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 5 weaker matches are hidden by default — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Efficient Multi-task LLM Quantization and Serving for Multiple LoRA Adapters
Adaptive Multi-task Learning for Few-Shot Object Detection
Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data
Approximate Forest Completion and Learning-Augmented Algorithms for Metric Minimum Spanning Trees
EduLLM: Leveraging Large Language Models and Framelet-Based Signed Hypergraph Neural Networks for Student Performance Prediction
KISA: A Unified Keyframe Identifier and Skill Annotator for Long-Horizon Robotics Demonstrations
@article{yang2026federated,
title = {Federated Natural Policy Gradient and Actor Critic Methods for Multi-task Reinforcement Learning},
author = {Tong Yang and Shicong Cen and Yuting Wei and Yuxin Chen and Yuejie Chi},
year = {2026},
url = {https://www.semanticscholar.org/paper/26de3def97d00311939a1cd6acbd3960eb775ad1},
journal = {NEURIPS 2024 2024}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/26de3def97d00311939a1cd6acbd3960eb775ad1)