exploration-exploitation
There are 43 repositories under exploration-exploitation topic.
wzhe06/Reco-papers
Classic papers and resources on recommendation
opendilab/DI-engine
OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.
tigerneil/awesome-deep-rl
For deep RL and the future of AI.
imsheridan/DeepRec
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
david-cortes/contextualbandits
Python implementations of contextual bandits algorithms
YaoYao1995/MEEE
Code to reproduce the experiments in Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation (MEEE).
opendilab/awesome-exploration-rl
A curated list of awesome exploration RL resources (continually updated)
TianhongDai/self-imitation-learning-pytorch
This is the pytorch implementation of ICML 2018 paper - Self-Imitation Learning.
holarissun/RewardShifting
Code for NeurIPS 2022 paper Exploiting Reward Shifting in Value-Based Deep RL
stratisMarkou/sample-efficient-bayesian-rl
Source for the sample efficient tabular RL submission to the 2019 NIPS workshop on Biological and Artificial RL
gokceuludogan/interactive-music-recommendation
Personalized and Interactive Music Recommendation with Bandit approach
Amshra267/Thompson-Greedy-Comparison-for-MultiArmed-Bandits
Repository Containing Comparison of two methods for dealing with Exploration-Exploitation dilemma for MultiArmed Bandits
kkm24132/ReinforcementLearning
Focuses on Reinforcement Learning related concepts, use cases, and learning approaches
kakaobrain/leco
Official implementation of LECO (NeurIPS'22)
baturaysaglam/DISCOVER
Deep Intrinsically Motivated Exploration in Continuous Control
guptav96/bandit-algorithms
A short implementation of bandit algorithms - ETC, UCB, MOSS and KL-UCB
hmishfaq/LMC-LSVI
The official code release for Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo, ICLR 2024.
haoyangzheng1996/ts_ulmc
The GitHub repository for "Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo", AISTATS 2024.
fabprezja/Deep-Learning-TPBook-Points
Some Key Points from the Deep Learning Tuning Playbook
hridayns/Research-Project-on-Reinforcement-learning
Research Thesis - Reinforcement Learning
kochlisGit/Reinforcement-Learning-Algorithms
This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.
Sagarnandeshwar/Bandit_Algorithms
Reinforcement Learning (COMP 579) Project
SXV357/Inspirit-AI-Deep-Dive-Designing-DL-Systems-FinalProject-RL-for-Autonomous-Vehicles
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)
panxulab/LSVI-ASE
The official code release for "More Efficient Randomized Exploration for Reinforcement Learning via Approximate Sampling", Reinforcement Learning Conference (RLC) 2024
Ralami1859/Action-Elimination-for-Multi-Armed-Bandits
Action elimination for multi-armed bandits
spoluan/reinforcement_learning
This repository contains a variety of projects related to reinforcement learning, showcasing different approaches to implementing it in various scenarios.
baturaysaglam/Q-Error-Exploration
An Optimistic Approach to the Q-Network Error in Actor-Critic Methods
kalexandriabond/competing-representations-shape-evidence-accumulation
Human and sim. behavioral / small-scale neural data for paper: https://www.biorxiv.org/content/10.1101/2022.10.03.510668v2
rom1mouret/exploration
over-parameterization = exploration ?
ruqoyyasadiq/deep_RL-multi-arm-bandit-exploration
This is an implementation of the Reinforcement Learning multi-arm-bandit experiment using different exploration techniques.
zwkcoding/explore_map_standalone
Maintain an environmental exploration map & Update by Bayesian probability **For Autonomous Vehicle**
avorozhtsov/shipit
Exploitation vs Exploration problem stated as A/B-testing with maximum profit per unit time.
KaranAnchan/10_Arm_Testbed
Explore the 10-Arm Testbed Simulation! 🎲 Utilize Python to test various ε-greedy strategies in a reinforcement learning environment. Visualize and compare agents' performance as they balance exploration and exploitation. Perfect for learners and enthusiasts! 🚀📊