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Meta-learning through hebbian plasticity in random networks
Lifelong learning and adaptability are two defining aspects of biological agents. Modern
reinforcement learning (RL) approaches have shown significant progress in solving complex …
reinforcement learning (RL) approaches have shown significant progress in solving complex …
Neuroevolution insights into biological neural computation
This article reviews existing work and future opportunities in neuroevolution, an area of
machine learning in which evolutionary optimization methods such as genetic algorithms …
machine learning in which evolutionary optimization methods such as genetic algorithms …
Evolving and merging hebbian learning rules: increasing generalization by decreasing the number of rules
Generalization to out-of-distribution (OOD) circumstances after training remains a challenge
for artificial agents. To improve the robustness displayed by plastic Hebbian neural …
for artificial agents. To improve the robustness displayed by plastic Hebbian neural …
Context meta-reinforcement learning via neuromodulation
Meta-reinforcement learning (meta-RL) algorithms enable agents to adapt quickly to tasks
from few samples in dynamic environments. Such a feat is achieved through dynamic …
from few samples in dynamic environments. Such a feat is achieved through dynamic …
Meta reinforcement learning with hebbian learning
Deep reinforcement learning achieves super-human performances at the cost of millions of
non-optimal interactions with environments. Ideally, a well trained deep reinforcement …
non-optimal interactions with environments. Ideally, a well trained deep reinforcement …
The configurable tree graph (ct-graph): measurable problems in partially observable and distal reward environments for lifelong reinforcement learning
A Soltoggio, E Ben-Iwhiwhu, C Peridis… - ar** reliable mechanisms for continuous local learning is a central challenge faced
by biological and artificial systems. Yet, how the environmental factors and structural …
by biological and artificial systems. Yet, how the environmental factors and structural …
Deep Reinforcement Learning for Combinatorial Optimization
D Wang - 2022 - search.proquest.com
Deep reinforcement learning proves its success in solving complicated combinatorial
problems. This work studies essential issues of DRL and its applications in the disassembly …
problems. This work studies essential issues of DRL and its applications in the disassembly …
Neuromodulated networks for lifelong learning and adaptation
E Ben-Iwhiwhu - 2023 - repository.lboro.ac.uk
The development of robust and adaptable intelligent system has been a long standing grand
challenge. Recently, machine learning methods via neural networks have gained …
challenge. Recently, machine learning methods via neural networks have gained …