11 citations · 31 across the 6 of their papers we have counts for
6 papers
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features Critics
Luca Grillotti, Maxence Faldor, Borja G. León +1
A key aspect of intelligence is the ability to demonstrate a broad spectrum of behaviors for adapting to unexpected situations. Over the past decade, advancements in deep reinforce…
Do Androids Dream of Electric Fences? Safety-Aware Reinforcement Learning with Latent Shielding
Chloe He, Borja G. Leon, Francesco Belardinelli
The growing trend of fledgling reinforcement learning systems making their way into real-world applications has been accompanied by growing concerns for their safety and robustness…
In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications
Borja G. León, Murray Shanahan, Francesco Belardinelli
We address the problem of building agents whose goal is to learn to execute out-of distribution (OOD) multi-task instructions expressed in temporal logic (TL) by using deep reinfor…
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors
Pierre El Mqirmi, Francesco Belardinelli, Borja G. León
Multi-agent reinforcement learning (RL) often struggles to ensure the safe behaviours of the learning agents, and therefore it is generally not adapted to safety-critical applicati…
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning
Borja G. León, Murray Shanahan, Francesco Belardinelli
This work introduces a neuro-symbolic agent that combines deep reinforcement learning (DRL) with temporal logic (TL) to achieve systematic zero-shot, i.e., never-seen-before, gener…
Extended Markov Games to Learn Multiple Tasks in Multi-Agent Reinforcement Learning
Borja G. León, Francesco Belardinelli
The combination of Formal Methods with Reinforcement Learning (RL) has recently attracted interest as a way for single-agent RL to learn multiple-task specifications. In this paper…