Lagrangian Duality in Reinforcement Learning

20 Jul 2020 Pranay Pasula

Although duality is used extensively in certain fields, such as supervised learning in machine learning, it has been much less explored in others, such as reinforcement learning (RL). In this paper, we show how duality is involved in a variety of RL work, from that which spearheaded the field, such as Richard Bellman's value iteration, to that which was done within just the past few years yet has already had significant impact, such as TRPO, A3C, and GAIL... (read more)

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Methods used in the Paper


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