Learning to Factor Policies and Action-Value Functions: Factored Action Space Representations for Deep Reinforcement learning

Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains. Among all such visual decision making problems, those with discrete action spaces often tend to have underlying compositional structure in the said action space... (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