Scalable and effective exploration remains a key challenge in reinforcement learning (RL).
While there are methods with optimality guarantees in the setting of discrete state and action …

Learning about many things can provide numerous benefits to a reinforcement learning
system. For example, learning many auxiliary value functions, in addition to optimizing the …

Markov Brains are a class of evolvable artificial neural networks (ANN). They differ from
conventional ANNs in many aspects, but the key difference is that instead of a layered …

Empowerment is an information-theoretic method that can be used to intrinsically motivate
learning agents. It attempts to maximize an agent's control over the environment by …

THE EVOLUTIONARY PATH TO SENTIENT MACHINES COLUMN The Elements of Intelligence
Page 1 THE EVOLUTIONARY PATH TO SENTIENT MACHINES COLUMN The Elements of …

Within the field of Genetic Algorithms (GA) and Artificial Intelligence (AI) a variety
computational substrates with the power to find solutions to a large variety of problems have …

There are two common approaches for optimizing the performance of a machine: genetic
algorithms and machine learning. A genetic algorithm is applied over many generations …

Artificial neural networks (ANNs), while exceptionally useful for classification, are vulnerable
to misdirection. Small amounts of noise can significantly affect their ability to correctly …

The QBIT theory is an attempt toward solving the problem of consciousness based on
empirical evidence provided by various scientific disciplines including quantum mechanics …

How cognitive neural systems process information is largely unknown, in part because of
how difficult it is to accurately follow the flow of information from sensors via neurons to …