33 citations · 34 across the 4 of their papers we have counts for
5 papers
Emergence of Adaptive Circadian Rhythms in Deep Reinforcement Learning
Aqeel Labash, Florian Fletzer, Daniel Majoral +1
Adapting to regularities of the environment is critical for biological organisms to anticipate events and plan. A prominent example is the circadian rhythm corresponding to the int…
Mind the gap: Challenges of deep learning approaches to Theory of Mind
Jaan Aru, Aqeel Labash, Oriol Corcoll +1
Theory of Mind is an essential ability of humans to infer the mental states of others. Here we provide a coherent summary of the potential, current progress, and problems of deep l…
Perspective Taking in Deep Reinforcement Learning Agents
Aqeel Labash, Jaan Aru, Tambet Matiisen +2
Perspective taking is the ability to take the point of view of another agent. This skill is not unique to humans as it is also displayed by other animals like chimpanzees. It is an…
APES: a Python toolbox for simulating reinforcement learning environments
Aqeel Labash, Ardi Tampuu, Tambet Matiisen +2
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years. The simulation environment in which the agents in…
Do deep reinforcement learning agents model intentions?
Tambet Matiisen, Aqeel Labash, Daniel Majoral +2
Inferring other agents' mental states such as their knowledge, beliefs and intentions is thought to be essential for effective interactions with other agents. Recently, multiagent…