Evolutionary robotics applies the selection, variation, and heredity principles of natural
evolution to the design of robots with embodied intelligence. It can be considered as a …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

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 …

Many fields use search algorithms, which automatically explore a search space to find high-
performing solutions: chemists search through the space of molecules to discover new …

Intrinsically motivated spontaneous exploration is a key enabler of autonomous
developmental learning in human children. It enables the discovery of skill repertoires …

Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its
benchmark problems are popular purely through historical contingency, and they can be …

Novelty search is a recent artificial evolution technique that challenges traditional
evolutionary approaches. In novelty search, solutions are rewarded based on their novelty …

Continuous action policy search is currently the focus of intensive research, driven both by
the recent success of deep reinforcement learning algorithms and the emergence of …

One of the long-term goals in evolutionary robotics is to be able to automatically synthesize
controllers for real autonomous robots based only on a task specification. While a number of …

The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima.
Novelty Search avoids this problem by encouraging a search in all interesting directions …