Classic papers and resources on recommendation

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

A curated list of awesome exploration RL resources (continually updated)

Code to reproduce the experiments in Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation (MEEE).

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

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)

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.

The official code release for "More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling", Reinforcement Learning Conference (RLC) 2024

This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.

Reinforcement Learning (COMP 579) Project

Some Key Points from the Deep Learning Tuning Playbook

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)