Evolution strategies (ES) are a family of black-box optimization algorithms able to train deep
neural networks roughly as well as Q-learning and policy gradient methods on challenging …

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in
complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a …

Deep neuroevolution, that is evolutionary policy search methods based on deep neural
networks, have recently emerged as a competitor to deep reinforcement learning algorithms …

Recently, we have seen a rapidly growing adoption of Deep Reinforcement Learning (DRL)
technologies. Fully achieving the promise of these technologies in practice is, however …

Reinforcement learning (RL) is a powerful technique for learning optimal policies from trial
and error. However, designing a reward function that captures the desired behavior of an …

Designing agents that acquire knowledge autonomously and use it to solve new tasks
efficiently is an important challenge in reinforcement learning. Knowledge acquired during …

To generate adaptive behaviors, animals must learn from their interactions with the
environment. Describing the algorithms that govern this learning process and how they are …

Reinforcement learning (RL) is designed to learn optimal control policies from unsupervised
interactions with the environment. Many successful RL algorithms have been developed …

Abstract Deep Reinforcement Learning (DRL) algorithms have been successfully applied to
a range of challenging control tasks. However, these methods typically suffer from three core …

OpenAI Gym is a toolkit for reinforcement learning research. It includes a growing collection
of benchmark problems that expose a common interface, and a website where people can …