TianyuCodings/Diffusion_Trusted_Q_Learning
[NeuIPS2024 DTQL] Diffusion Trusted Q-Learning for Offline RL — Official PyTorch Implementation
This project helps machine learning researchers and practitioners who are developing offline reinforcement learning agents. It takes pre-collected datasets of actions and rewards and outputs highly efficient, optimized policies for tasks where data collection is expensive or unsafe. The end user is typically an AI/ML researcher or engineer working on advanced control systems or decision-making algorithms.
No commits in the last 6 months.
Use this if you need to train robust and high-performing offline reinforcement learning policies without needing to collect new data, especially for tasks requiring fast inference and training.
Not ideal if your primary goal is online reinforcement learning where direct interaction with the environment is possible and preferred.
Stars
23
Forks
2
Language
Python
License
—
Category
Last pushed
May 31, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/diffusion/TianyuCodings/Diffusion_Trusted_Q_Learning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
ZhengYinan-AIR/Diffusion-Planner
[ICLR 2025 Oral] The official implementation of "Diffusion-Based Planning for Autonomous Driving...
intuitive-robots/MoDE_Diffusion_Policy
[ICLR 25] Code for "Efficient Diffusion Transformer Policies with Mixture of Expert Denoisers...
caio-freitas/GraphDiffusionImitate
Diffusion-based graph generative policies for imitation learning in robotics tasks 🧠🤖
LeCAR-Lab/model-based-diffusion
Official implementation for the paper "Model-based Diffusion for Trajectory Optimization"....
Weixy21/SafeDiffuser
Safe Planning with Diffusion Probabilistic Models