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aeirl-pytorch

A simple implementation of Auto-Encoding Inverse Reinforcement Learning with PyTorch

asym-rlpo

Asymmetric methods for partially observable reinforcement learning

bayesian-network-learning

Comparing learning algorithms based on constraints (in progress).

centrale-sdm-23-24

Sequential Decision Making in Central, based on RLSS 2019

crossentropymethod-vs-policygradient

Simple library based on https://github.com/osigaud/Basic-Policy-Gradient-Labs made for the Master's project: Comparison of Evolutionnary Methods and Reinforcement Learning methods on classical control benchmarks. Supervised by Prof Olivier Sigaud with Damien Legros.

forecasting-rnn

A simple time series forecasting library using RNNs

gym-decision-trees

Simple continuous-states mdp generator for which the optimal policy is a decision tree of given depth. Interface with openAI Gym.

high-dim-covariance-matrix-estimation

A small library to study some Covariance Matrix Estimators in R. Designed in 2019 as part of my Bachelor's Degree thesis.

homepage

lunarlander-v2

Using Stable-Baselines 3 library to train a LunarLander-v2 agent. With Yannis Elrharbi-Fleury : https://github.com/sohio92

marionette-pong

Trying MarioNette on Pong

mnist-deeplearning

Code image classification of MNIST dataset using different architectures: simple linear NN, autoencoder, and highway network

morrispool-reinforcementlearning

Code and PDF report about reproducing the Morris Pool experiment: model rats behaviour in a pool using reinforcement learning

pareto-local-search

PLS algo for knapsack problem.

primenet

Predicting prime numbers as list of bits.

prioritized-experience-replay

Implementation of Dyna-Q according to Sutton and Barto. Implementation of Mattar's learning model: https://www.nature.com/articles/s41593-018-0232-z

rlberry

An easy-to-use reinforcement learning library for research and education.

segment-anything-gym-wrapper

A gymnasium wrapper for objects states detected with the Segment Anything Model.

teachings

Some exercises for the class of Reinforcement Learning at University of Lille

tree-mbpo

Study Model-Based Policy Optimization by varying the model estimator classes (e.g Decision Trees vs MLP)

trees-programs-videos-iqdagger

trex-tree-reward-exploration

Using Tree estimators of the MDP models to then count leaves grouping similar transitions and do count-based exploration.

tuto-interpreter