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
For deep RL and the future of AI.
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
Python implementations of contextual bandits algorithms
Code to reproduce the experiments in Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation (MEEE).
A curated list of awesome exploration RL resources (continually updated)
This is the pytorch implementation of ICML 2018 paper - Self-Imitation Learning.
Source for the sample efficient tabular RL submission to the 2019 NIPS workshop on Biological and Artificial RL
Code for NeurIPS 2022 paper Exploiting Reward Shifting in Value-Based Deep RL
Personalized and Interactive Music Recommendation with Bandit approach
Repository Containing Comparison of two methods for dealing with Exploration-Exploitation dilemma for MultiArmed Bandits
Focuses on Reinforcement Learning related concepts, use cases, and learning approaches
Official implementation of LECO (NeurIPS'22)
Deep Intrinsically Motivated Exploration in Continuous Control
A short implementation of bandit algorithms - ETC, UCB, MOSS and KL-UCB
The official code release for Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo, ICLR 2024.
Research Thesis - Reinforcement Learning
Some Key Points from the Deep Learning Tuning Playbook
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