Equivalence Between Policy Gradients and Soft Q-Learning

21 Apr 2017 John Schulman Xi Chen Pieter Abbeel

Two of the leading approaches for model-free reinforcement learning are policy gradient methods and $Q$-learning methods. $Q$-learning methods can be effective and sample-efficient when they work, however, it is not well-understood why they work, since empirically, the $Q$-values they estimate are very inaccurate... (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