MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with many different methods, networks and hyperparameters settings without requiring sophisticated software or exceedingly long training times. Baseline agents for major learning algorithms such as DDPG, PPO, SAC, TD3 and DSAC are provided too, along with a preliminary comparison in terms of training time and performance.
MicroRacer: A Didactic Environment for Deep Reinforcement Learning / Asperti, Andrea; Del Brutto, Marco. - STAMPA. - 13810:(2023), pp. 239-252. (Intervento presentato al convegno International Conference on Machine Learning, Optimization, and Data Science - LOD 2022 tenutosi a Certosa di Pontignano, Siena, Italy nel 18-22 settembre 2022) [10.1007/978-3-031-25599-1_18].
MicroRacer: A Didactic Environment for Deep Reinforcement Learning
Asperti, Andrea
;
2023
Abstract
MicroRacer is a simple, open source environment inspired by car racing especially meant for the didactics of Deep Reinforcement Learning. The complexity of the environment has been explicitly calibrated to allow users to experiment with many different methods, networks and hyperparameters settings without requiring sophisticated software or exceedingly long training times. Baseline agents for major learning algorithms such as DDPG, PPO, SAC, TD3 and DSAC are provided too, along with a preliminary comparison in terms of training time and performance.| File | Dimensione | Formato | |
|---|---|---|---|
|
LOD_2022_Camera-Ready_2300.pdf
Open Access dal 10/03/2024
Tipo:
Postprint
Licenza:
Licenza per accesso libero gratuito
Dimensione
559.93 kB
Formato
Adobe PDF
|
559.93 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
