13 citations · 14 across the 7 of their papers we have counts for
7 papers
Semantic Partial Grounding via LLMs
Giuseppe Canonaco, Alberto Pozanco, Daniel Borrajo
Grounding is a critical step in classical planning, yet it often becomes a computational bottleneck due to the exponential growth in grounded actions and atoms as task size increas…
GenPlanX. Generation of Plans and Execution
Daniel Borrajo, Giuseppe Canonaco, Tomás de la Rosa +10
Classical AI Planning techniques generate sequences of actions for complex tasks. However, they lack the ability to understand planning tasks when provided using natural language.…
Projection Abstractions in Planning Under the Lenses of Abstractions for MDPs
Giuseppe Canonaco, Alberto Pozanco, Daniel Borrajo
The concept of abstraction has been independently developed both in the context of AI Planning and discounted Markov Decision Processes (MDPs). However, the way abstractions are bu…
On Learning Action Costs from Input Plans
Marianela Morales, Alberto Pozanco, Giuseppe Canonaco +3
Most of the work on learning action models focus on learning the actions' dynamics from input plans. This allows us to specify the valid plans of a planning task. However, very lit…
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…
Time-Variant Variational Transfer for Value Functions
Giuseppe Canonaco, Andrea Soprani, Manuel Roveri +1
In most of the transfer learning approaches to reinforcement learning (RL) the distribution over the tasks is assumed to be stationary. Therefore, the target and source tasks are i…
Stochastic Variance-Reduced Policy Gradient
Matteo Papini, Damiano Binaghi, Giuseppe Canonaco +2
In this paper, we propose a novel reinforcement- learning algorithm consisting in a stochastic variance-reduced version of policy gradient for solving Markov Decision Processes (MD…