Classic papers and resources on recommendation
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Updated
Jun 13, 2020 - Python
Classic papers and resources on recommendation
OpenDILab Decision AI Engine
推荐、广告工业界经典以及最前沿的论文、资料集合/ Must-read Papers on Recommendation System and CTR Prediction
For deep RL and the future of AI.
Python implementations of contextual bandits algorithms
Code to reproduce the experiments in Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation (MEEE).
Source for the sample efficient tabular RL submission to the 2019 NIPS workshop on Biological and Artificial RL
This is the pytorch implementation of ICML 2018 paper - Self-Imitation Learning.
A curated list of awesome exploration RL resources (continually updated)
Maintain an environmental exploration map & Update by Bayesian probability **For Autonomous Vehicle**
Personalized and Interactive Music Recommendation with Bandit approach
Focuses on Reinforcement Learning related concepts, use cases, and learning approaches
The official code release for Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo, ICLR 2024.
Action elimination for multi-armed bandits
Repository for our paper: "Improving Reinforcement Learning Exploration with Causal Models of Core Environment Dynamics". (submitted to ECAI 2024)
Research Thesis - Reinforcement Learning
Some Key Points from the Deep Learning Tuning Playbook
Code for NeurIPS 2022 paper Exploiting Reward Shifting in Value-Based Deep RL
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