The reward functions that drive reinforcement learning systems are generally derived
directly from the descriptions of the problems that the systems are being used to solve. In …

In this paper, we investigate the use of nested evolution in which each step of one
evolutionary process involves running a second evolutionary process. We apply this …

In both the natural and artificial realms, evolution and reinforcement learning are parallel
adaptive processes that work on different scales but with similar feedback mechanisms. This …

Reinforcement learning (RL) problems come in many flavours, as do algorithms for solving
them. It is currently not clear which of the commonly used RL benchmarks best measure an …

Reinforcement Learning (RL) has proven to be highly effective in various real-world
applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been …

Abstract Evolutionary Reinforcement Learning (ERL) has garnered widespread attention in
recent years due to its inherent robustness and parallelism. However, the integration of …

Solving real-life sequential decision making problems under partial observability involves an
exploration-exploitation problem. To be successful, an agent needs to efficiently gather …

Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …

Modern artificial intelligence works typically train the parameters of fixed-sized deep neural
networks using gradient-based optimization techniques. Simple evolutionary algorithms …

Deep reinforcement learning algorithms have been successfully applied to a range of
challenging control tasks. However, these methods typically struggle with achieving effective …