Deep NeuroEvolution
A collection of neuroevolution experiments for reinforcement learning control problems using unsupervised learning feature extractors.
What is Deep NeuroEvolution?
DNE (Deep Neuroevolution Experiments) is a research project that implements various neuroevolution algorithms for training deep neural networks in reinforcement learning environments. It focuses on combining unsupervised learning feature extractors with evolutionary reinforcement learning approaches to solve control problems more efficiently. The project includes implementations of algorithms like UL-ELR, BD-NES, and RNES for tasks ranging from classic control problems to Atari game playing.
Machine learning researchers and practitioners interested in neuroevolution, evolutionary algorithms, and reinforcement learning who want to experiment with combining unsupervised learning with evolutionary approaches.
DNE provides a specialized collection of neuroevolution algorithms specifically designed for deep reinforcement learning, with a focus on sample efficiency and the novel combination of unsupervised feature learning with evolutionary optimization techniques.
Overview
A set of neuroevolution experiments with/towards deep networks
Use Cases
Best For
- Researching neuroevolution approaches for deep reinforcement learning
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