15 research outputs found
Perspective Taking in Deep Reinforcement Learning Agents
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 essential ability for social interactions, including
efficient cooperation, competition, and communication. Here we present our
progress toward building artificial agents with such abilities. We implemented
a perspective taking task inspired by experiments done with chimpanzees. We
show that agents controlled by artificial neural networks can learn via
reinforcement learning to pass simple tests that require perspective taking
capabilities. We studied whether this ability is more readily learned by agents
with information encoded in allocentric or egocentric form for both their
visual perception and motor actions. 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
Biases for Emergent Communication in Multi-agent Reinforcement Learning
We study the problem of emergent communication, in which language arises
because speakers and listeners must communicate information in order to solve
tasks. In temporally extended reinforcement learning domains, it has proved
hard to learn such communication without centralized training of agents, due in
part to a difficult joint exploration problem. We introduce inductive biases
for positive signalling and positive listening, which ease this problem. In a
simple one-step environment, we demonstrate how these biases ease the learning
problem. We also apply our methods to a more extended environment, showing that
agents with these inductive biases achieve better performance, and analyse the
resulting communication protocols.Comment: Accepted at NeurIPS 201