[NeurIPS2021] Federated Reinforcement Learning with Theoretical Guarantees. The repo contains code and experiments for our Federated Policy Gradient with Byzantine Resilience framework for improving sample efficiency of RL agents.

A paper list of sample-efficient reinforcement learning

This repository is the official implementation of the paper "SHIRE: Enhancing Sample Efficiency using Human Intuition in Reinforcement Learning" (ICRA 2025)

Implementation of our IJCAI 2022 paper "CCLF: A Contrastive-Curiosity-Driven Learning Framework for Sample-Efficient Reinforcement Learning".

Collection of codes pertaining to my research in model-free RL algorithms.

[RLC2025] - Official repository of "SPEQ: Offline Stabilization Phases for Efficient Q-Learning in High Update-To-Data Reinforcement Learning"

[NeurIPS 2023] CycAug implementation from paper 'Learning Better with Less: Effective Augmentation for Sample-Efficient Visual RL'.

Official code for "Representation Learning Enables Scalable Multitask Deep Reinforcement Learning".

Bachelor Thesis: Sample-Efficient Bayesian Model-Based Reinforcement Learning Using DBN and PPL Models

Official repository of "SOPE: Stabilizing Off-Policy Evaluation for Online RL with Prior Data"

This repository contains the training and evaluation code accompanying the thesis "Learning with Less: Contrastive Weight Tying on the BabyLM Challenge" (Ino van de Wouw, VU Amsterdam, 2025). The project studies headless language models, models pretrained with Contrastive Weight Tying (CWT) (Godey et al., 2024) instead of a standard cross-entropy

PyTorch implementation (with up-to-date tooling) of the SAM / DAC algorithm