Classic papers and resources on recommendation
-
Updated
Jun 13, 2020 - Python
Classic papers and resources on recommendation
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
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.
Code for NeurIPS 2022 paper Exploiting Reward Shifting in Value-Based Deep RL
Source for the sample efficient tabular RL submission to the 2019 NIPS workshop on Biological and Artificial 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)
A short implementation of bandit algorithms - ETC, UCB, MOSS and KL-UCB
Deep Intrinsically Motivated Exploration in Continuous Control
The official code release for Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo, ICLR 2024.
The GitHub repository for "Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo", AISTATS 2024.
This project uses Reinforcement Learning to teach an agent to drive by itself and learn from its observations so that it can maximize the reward(180+ lines)
Add a description, image, and links to the exploration-exploitation topic page so that developers can more easily learn about it.
To associate your repository with the exploration-exploitation topic, visit your repo's landing page and select "manage topics."