In addition to their undisputed success in solving classical optimization problems,
neuroevolutionary and population-based algorithms have become an alternative to standard …

Exploration is a key problem in reinforcement learning, since agents can only learn from
data they acquire in the environment. With that in mind, maintaining a population of agents is …

In this work, we propose a novel approach for reinforcement learning driven by evolutionary
computation. Our algorithm, dubbed as Evolutionary-Driven Reinforcement Learning (Evo …

Reinforcement Learning (RL) methods often rely on gradient estimates to learn an optimal
policy for control problems. These expensive computations result in long training times, a …

Algorithms for evolutionary computation, which simulate the process of natural selection to
solve optimization problems, are an effective tool for discovering high-performing …

Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective
at solving reinforcement learning (RL) problems. However, since few rigorous empirical …

Reinforcement Learning (RL) has achieved impressive performance in many complex
environments due to the integration with Deep Neural Networks (DNNs). At the same time …

In recent years, brain science and neuroscience have greatly propelled the innovation of
computer science. In particular, knowledge from the neurobiology and neuropsychology of …

Neuroevolution, ie evolution of artificial neural networks, has recently emerged as a
powerful technique for solving challenging reinforcement learning problems. Compared to …

Recently, there has been a focus on using transfer learning to reduce the sample complexity
in reinforcement learning. One component that enables transfer is an intertask mapping that …