exploration-exploitation

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

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.

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

The official code release for "Langevin Soft Actor-Critic: Efficient Exploration through Uncertainty-Driven Critic Learning", ICLR 2025

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.

Deep Intrinsically Motivated Exploration in Continuous Control

A short implementation of bandit algorithms - ETC, UCB, MOSS and KL-UCB

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