This is a pip package implementing Reinforcement Learning algorithms in non-stationary environments supported by the OpenAI Gym toolkit.

Continual Reinforcement Learning in 3D Non-stationary Environments

Queue-Based Resampling (QBR, ICANN 2018)

Repo for course CSC2558: "Intelligent Adaptive Interventions" project in nonstationary contextual bandits.

The implementation of the Diversity Pool algorithm, proposed in the paper "Diversity-Based Pool of Models for Dealing with Recurring Concepts" and presented at IJCNN '18

Matlab laboratories for the course "Online Learning and Monitoring" at Politecnico di Milano

Code associated with the NeurIPS19 paper "Weighted Linear Bandits in Non-Stationary Environments"

Mitigating the Stability-Plasticity Dilemma in Adaptive Train Scheduling with Curriculum-Driven Continual DQN Expansion

Experiments for paper "Online Learning with Costly Features in Non-stationary Environments"

Work for CDC2020

R package to apply the transformed-stationary extreme value analysis

Code for the manuscript 'Hierarchy of prediction errors shapes the learning of context-dependent sensory representations'