Do deep reinforcement learning agents model intentions?

1 code implementation • 15 May 2018 • Tambet Matiisen, Aqeel Labash, Daniel Majoral, Jaan Aru, Raul Vicente

In this work we test whether deep reinforcement learning agents explicitly represent other agents' intentions (their specific aims or goals) during a task in which the agents had to coordinate the covering of different spots in a 2D environment.

reinforcement-learning Reinforcement Learning (RL)

APES: a Python toolbox for simulating reinforcement learning environments

2 code implementations • 31 Aug 2018 • Aqeel Labash, Ardi Tampuu, Tambet Matiisen, Jaan Aru, Raul Vicente

Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years.

reinforcement-learning Reinforcement Learning (RL)

Perspective Taking in Deep Reinforcement Learning Agents

no code implementations • 3 Jul 2019 • Aqeel Labash, Jaan Aru, Tambet Matiisen, Ardi Tampuu, Raul Vicente

We believe that, in the long run, building better artificial agents with perspective taking ability can help us develop artificial intelligence that is more human-like and easier to communicate with.

reinforcement-learning Reinforcement Learning (RL)

Mind the gap: Challenges of deep learning approaches to Theory of Mind

no code implementations • 30 Mar 2022 • Jaan Aru, Aqeel Labash, Oriol Corcoll, Raul Vicente

Theory of Mind is an essential ability of humans to infer the mental states of others.

Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning

1 code implementation • 22 Jul 2023 • Aqeel Labash, Florian Fletzer, Daniel Majoral, Raul Vicente

From a dynamical systems view, we demonstrate that the adaptation proceeds by the emergence of a stable periodic orbit in the neuron dynamics with a phase response that allows an optimal phase synchronisation between the agent's dynamics and the environmental rhythm.

reinforcement-learning