[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 contains code associated with 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'.

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

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

Reinforcement learning framework for environments with action-order invariance to achieve sample efficiency gains