Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) algorithms are known to be data inefficient. One reason is that a DRL agent learns both the feature and the policy tabula rasa... (read more)

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Methods used in the Paper


METHOD TYPE
Entropy Regularization
Regularization
Dense Connections
Feedforward Networks
Softmax
Output Functions
Convolution
Convolutions
A3C
Policy Gradient Methods