Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX

1 code implementation • 16 Jun 2023 • Clément Bonnet, Daniel Luo, Donal Byrne, Shikha Surana, Sasha Abramowitz, Paul Duckworth, Vincent Coyette, Laurence I. Midgley, Elshadai Tegegn, Tristan Kalloniatis, Omayma Mahjoub, Matthew Macfarlane, Andries P. Smit, Nathan Grinsztajn, Raphael Boige, Cemlyn N. Waters, Mohamed A. Mimouni, Ulrich A. Mbou Sob, Ruan de Kock, Siddarth Singh, Daniel Furelos-Blanco, Victor Le, Arnu Pretorius, Alexandre Laterre

Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms.

Decision Making reinforcement-learning +1

Learning Reward Machines in Cooperative Multi-Agent Tasks

no code implementations • 24 Mar 2023 • 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 the structure of the sub-tasks.

Multi-agent Reinforcement Learning

Winner Takes It All: Training Performant RL Populations for Combinatorial Optimization

1 code implementation • NeurIPS 2023 • Nathan Grinsztajn, Daniel Furelos-Blanco, Shikha Surana, Clément Bonnet, Thomas D. Barrett

Applying reinforcement learning (RL) to combinatorial optimization problems is attractive as it removes the need for expert knowledge or pre-solved instances.

Combinatorial Optimization Job Shop Scheduling +2

Hierarchies of Reward Machines

1 code implementation • 31 May 2022 • Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo

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 task using high-level events.

Induction and Exploitation of Subgoal Automata for Reinforcement Learning

no code implementations • 8 Sep 2020 • Daniel Furelos-Blanco, Mark Law, Anders Jonsson, Krysia Broda, Alessandra Russo

In this paper we present ISA, an approach for learning and exploiting subgoals in episodic reinforcement learning (RL) tasks.

Inductive logic programming reinforcement-learning +1

Induction of Subgoal Automata for Reinforcement Learning

no code implementations • 29 Nov 2019 • Daniel Furelos-Blanco, Mark Law, Alessandra Russo, Krysia Broda, Anders Jonsson

In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL).

Inductive logic programming reinforcement-learning +2

Solving Multiagent Planning Problems with Concurrent Conditional Effects

1 code implementation • 19 Jun 2019 • Daniel Furelos-Blanco, Anders Jonsson

Empirically, we show that our compilation can solve challenging multiagent planning problems that require concurrent actions.