Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Nodari, Lorenzo"'
Autor:
Parac, Roko, Nodari, Lorenzo, Ardon, Leo, Furelos-Blanco, Daniel, Cerutti, Federico, Russo, Alessandra
This paper presents PROB-IRM, an approach that learns robust reward machines (RMs) for reinforcement learning (RL) agents from noisy execution traces. The key aspect of RM-driven RL is the exploitation of a finite-state machine that decomposes the ag
Externí odkaz:
http://arxiv.org/abs/2408.14871
Autor:
Nodari, Lorenzo, Cerutti, Federico
Robustness to noise is of utmost importance in reinforcement learning systems, particularly in military contexts where high stakes and uncertain environments prevail. Noise and uncertainty are inherent features of military operations, arising from fa
Externí odkaz:
http://arxiv.org/abs/2311.09027
Autor:
Nodari, Lorenzo
In recent years, Reward Machines (RMs) have stood out as a simple yet effective automata-based formalism for exposing and exploiting task structure in reinforcement learning settings. Despite their relevance, little to no attention has been directed
Externí odkaz:
http://arxiv.org/abs/2311.09014