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
Python implementations of contextual bandits algorithms
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
Repository Containing Comparison of two methods for dealing with Exploration-Exploitation dilemma for MultiArmed Bandits
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 GitHub repository for "Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte Carlo", AISTATS 2024.
The official code release for Provable and Practical: Efficient Exploration in Reinforcement Learning via Langevin Monte Carlo, ICLR 2024.
Research Thesis - Reinforcement Learning
This project focuses on comparing different Reinforcement Learning Algorithms, including monte-carlo, q-learning, lambda q-learning epsilon-greedy variations, etc.
An Optimistic Approach to the Q-Network Error in Actor-Critic Methods
over-parameterization = exploration ?
Repository for our paper: "Improving Reinforcement Learning Exploration with Causal Models of Core Environment Dynamics". (submitted to ECAI 2024)
This is an implementation of the Reinforcement Learning multi-arm-bandit experiment using different exploration techniques.
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