21 citations · 24 across the 6 of their papers we have counts for
8 papers
Beyond Fixed Tasks: Unsupervised Environment Design for Task-Level Pairs
Daniel Furelos-Blanco, Charles Pert, Frederik Kelbel +3
Training general agents to follow complex instructions (tasks) in intricate environments (levels) remains a core challenge in reinforcement learning. Random sampling of task-level…
FORM: Learning Expressive and Transferable First-Order Logic Reward Machines
Leo Ardon, Daniel Furelos-Blanco, Roko Parac +1
Reward machines (RMs) are an effective approach for addressing non-Markovian rewards in reinforcement learning (RL) through finite-state machines. Traditional RMs, which label edge…
Learning Robust Reward Machines from Noisy Labels
Roko Parac, Lorenzo Nodari, Leo Ardon +3
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 R…
Learning Reward Machines in Cooperative Multi-Agent Tasks
Leo Ardon, Daniel Furelos-Blanco, Alessandra Russo
This paper presents a novel approach to Multi-Agent Reinforcement Learning (MARL) that combines cooperative task decomposition with the learning of reward machines (RMs) encoding t…
Hierarchies of Reward Machines
Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2
Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the…
Induction and Exploitation of Subgoal Automata for Reinforcement Learning
Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2
In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks. ISA interleaves reinforcement learning with the induct…
Induction of Subgoal Automata for Reinforcement Learning
Daniel Furelos-Blanco, Mark Law, Alessandra Russo +2
In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL). Our method relies on inducing an automaton whose transitions are…
Solving Multiagent Planning Problems with Concurrent Conditional Effects
Daniel Furelos-Blanco, Anders Jonsson
In this work we present a novel approach to solving concurrent multiagent planning problems in which several agents act in parallel. Our approach relies on a compilation from concu…