Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

Deep artificial neural networks (DNNs) are typically trained via gradient-based learning algorithms, namely backpropagation. Evolution strategies (ES) can rival backprop-based algorithms such as Q-learning and policy gradients on challenging deep reinforcement learning (RL) problems... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization
Convolution
Convolutions
Softmax
Output Functions
A3C
Policy Gradient Methods
Dense Connections
Feedforward Networks
DQN
Q-Learning Networks
Q-Learning
Off-Policy TD Control