Proximal Distilled Evolutionary Reinforcement Learning

24 Jun 2019 Cristian Bodnar Ben Day Pietro Lió

Reinforcement Learning (RL) has achieved impressive performance in many complex environments due to the integration with Deep Neural Networks (DNNs). At the same time, Genetic Algorithms (GAs), often seen as a competing approach to RL, had limited success in scaling up to the DNNs required to solve challenging tasks... (read more)

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Methods used in the Paper


METHOD TYPE
Adam
Stochastic Optimization
Clipped Double Q-learning
Off-Policy TD Control
ReLU
Activation Functions
Target Policy Smoothing
Regularization
Experience Replay
Replay Memory
Dense Connections
Feedforward Networks
TD3
Policy Gradient Methods
Entropy Regularization
Regularization
PPO
Policy Gradient Methods