A survey on evolutionary reinforcement learning algorithms
Reinforcement Learning (RL) has proven to be highly effective in various real-world
applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been …
applications. However, in certain scenarios, Evolutionary Algorithms (EAs) have been …
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
Combining evolution and deep reinforcement learning for policy search: A survey
O Sigaud - ACM Transactions on Evolutionary Learning, 2023 - dl.acm.org
Deep neuroevolution and deep Reinforcement Learning have received a lot of attention
over the past few years. Some works have compared them, highlighting their pros and cons …
over the past few years. Some works have compared them, highlighting their pros and cons …
Evolutionary learning of interpretable decision trees
LL Custode, G Iacca - IEEE Access, 2023 - ieeexplore.ieee.org
In the last decade, reinforcement learning (RL) has been used to solve several tasks with
human-level performance. However, there is a growing demand for interpretable RL, ie …
human-level performance. However, there is a growing demand for interpretable RL, ie …
Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms
Evolutionary Reinforcement Learning (ERL), which integrates Evolutionary Algorithms (EAs)
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
and Reinforcement Learning (RL) for optimization, has demonstrated remarkable …
How the morphology encoding influences the learning ability in body-brain co-optimization
Embedding the learning of controllers within the evolution of morphologies has emerged as
an effective strategy for the co-optimization of agents' bodies and brains. Intuitively, that is …
an effective strategy for the co-optimization of agents' bodies and brains. Intuitively, that is …
A co-evolutionary approach to interpretable reinforcement learning in environments with continuous action spaces
LL Custode, G Iacca - 2021 IEEE Symposium Series on …, 2021 - ieeexplore.ieee.org
Machine learning (ML) has lately achieved impressive breakthroughs in several fields,
enabling a plethora of exciting applications. However, mainstream ML techniques often …
enabling a plethora of exciting applications. However, mainstream ML techniques often …
Quality diversity evolutionary learning of decision trees
Addressing the need for explainable Machine Learning has emerged as one of the most
important research directions in modern Artificial Intelligence (AI). While the current …
important research directions in modern Artificial Intelligence (AI). While the current …
A population-based approach for multi-agent interpretable reinforcement learning
Abstract Multi-Agent Reinforcement Learning (MARL) made significant progress in the last
decade, mainly thanks to the major developments in the field of Deep Neural Networks …
decade, mainly thanks to the major developments in the field of Deep Neural Networks …
[HTML][HTML] Quality–diversity optimization of decision trees for interpretable reinforcement learning
Abstract In the current Artificial Intelligence (AI) landscape, addressing explainability and
interpretability in Machine Learning (ML) is of critical importance. In fact, the vast majority of …
interpretability in Machine Learning (ML) is of critical importance. In fact, the vast majority of …