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cs.LG2024★ 1 cited
On the Sample Efficiency of Abstractions and Potential-Based Reward Shaping in Reinforcement Learning
Giuseppe Canonaco, Leo Ardon, Alberto Pozanco +1
The use of Potential-Based Reward Shaping (PBRS) has shown great promise in the ongoing research effort to tackle sample inefficiency in Reinforcement Learning (RL). However, choos…
cs.LG2022
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning
Leo Ardon, Alberto Pozanco, Daniel Borrajo +1
Reinforcement Learning (RL) algorithms are known to scale poorly to environments with many available actions, requiring numerous samples to learn an optimal policy. The traditional…