Search Results for author:
Found 13 papers, 8 papers with code
Multiagent Cooperation and Competition with Deep Reinforcement Learning
In the present work we extend the Deep Q-Learning Network architecture proposed by Google DeepMind to multiagent environments and investigate how two agents controlled by independent Deep Q-Networks interact in the classic videogame Pong.
What deep learning can tell us about higher cognitive functions like mindreading?
Can deep learning (DL) guide our understanding of computations happening in biological brain?
Do deep reinforcement learning agents model intentions?
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.
APES: a Python toolbox for simulating reinforcement learning environments
Assisted by neural networks, reinforcement learning agents have been able to solve increasingly complex tasks over the last years.
MAXENT3D_PID: An Estimator for the Maximum-entropy Trivariate Partial Information Decomposition
Chicharro (2017) introduced a procedure to determine multivariate partial information measures within the maximum entropy framework, separating unique, redundant, and synergistic components of information.
Computation Optimization and Control
Perspective Taking in Deep Reinforcement Learning Agents
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.
Direct information transfer rate optimisation for SSVEP-based BCI
The proposed method shows good performance in classifying targets of a BCI, outperforming previously reported results on the same dataset by a factor of 2 in terms of ITR.
The many faces of deep learning
This diversity of perspectives on deep learning, from neuroscience to statistical physics, is a rich source of inspiration that fuels novel developments in the theory and applications of machine learning.
Disentangling causal effects for hierarchical reinforcement learning
This distribution is used by a high-level policy to 1) explore the environment via random effect exploration so that novel effects are continuously discovered and learned, and to 2) learn task-specific behavior by prioritizing the effects that maximize a given reward function.
Deep Neural Networks using a Single Neuron: Folded-in-Time Architecture using Feedback-Modulated Delay Loops
We present a method for folding a deep neural network of arbitrary size into a single neuron with multiple time-delayed feedback loops.
Did I do that? Blame as a means to identify controlled effects in reinforcement learning
This work proposes Controlled Effect Network (CEN), an unsupervised method based on counterfactual measures of blame to identify effects on the environment controlled by the agent.
Mind the gap: Challenges of deep learning approaches to Theory of Mind
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
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.
