Using Monte Carlo Tree Search as a Demonstrator within Asynchronous Deep RL

30 Nov 2018 Bilal Kartal Pablo Hernandez-Leal Matthew E. Taylor

Deep reinforcement learning (DRL) has achieved great successes in recent years with the help of novel methods and higher compute power. However, there are still several challenges to be addressed such as convergence to locally optimal policies and long training times... (read more)

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Methods used in the Paper


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