1 citations · 3 across the 9 of their papers we have counts for
10 papers
Continual Evolution Strategies in Control Tasks
Nicola Pitzalis, Eleni Nisioti, Antonio Carta +2
We study Evolution Strategies (ES) for continual control, where agents must adapt to changing tasks without forgetting previous ones. On sequential MuJoCo locomotion tasks, naive E…
FAST: Similarity-based Knowledge Transfer for Efficient Policy Learning
Alessandro Capurso, Elia Piccoli, Davide Bacciu
Transfer Learning (TL) offers the potential to accelerate learning by transferring knowledge across tasks. However, it faces critical challenges such as negative transfer, domain a…
Combining Pre-Trained Models for Enhanced Feature Representation in Reinforcement Learning
Elia Piccoli, Malio Li, Giacomo Carfì +2
The recent focus and release of pre-trained models have been a key components to several advancements in many fields (e.g. Natural Language Processing and Computer Vision), as a ma…
Learning and Transferring Physical Models through Derivatives
Alessandro Trenta, Andrea Cossu, Davide Bacciu
We propose Derivative Learning (DERL), a supervised approach that models physical systems by learning their partial derivatives. We also leverage DERL to build physical models incr…
Lifelong Evolution of Swarms
Lorenzo Leuzzi, Simon Jones, Sabine Hauert +2
Adapting to task changes without forgetting previous knowledge is a key skill for intelligent systems, and a crucial aspect of lifelong learning. Swarm controllers, however, are ty…
I Know How: Combining Prior Policies to Solve New Tasks
Malio Li, Elia Piccoli, Vincenzo Lomonaco +1
Multi-Task Reinforcement Learning aims at developing agents that are able to continually evolve and adapt to new scenarios. However, this goal is challenging to achieve due to the…
MultiSTOP: Solving Functional Equations with Reinforcement Learning
Alessandro Trenta, Davide Bacciu, Andrea Cossu +1
We develop MultiSTOP, a Reinforcement Learning framework for solving functional equations in physics. This new methodology produces actual numerical solutions instead of bounds on…
Calibration of Continual Learning Models
Lanpei Li, Elia Piccoli, Andrea Cossu +2
Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowled…
Adaptive Hyperparameter Optimization for Continual Learning Scenarios
Rudy Semola, Julio Hurtado, Vincenzo Lomonaco +1
Hyperparameter selection in continual learning scenarios is a challenging and underexplored aspect, especially in practical non-stationary environments. Traditional approaches, suc…
Avalanche RL: a Continual Reinforcement Learning Library
Nicolò Lucchesi, Antonio Carta, Vincenzo Lomonaco +1
Continual Reinforcement Learning (CRL) is a challenging setting where an agent learns to interact with an environment that is constantly changing over time (the stream of experienc…