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| | DuelingDQN () |
| | Default constructor. More...
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| | DuelingDQN (const DuelingDQN &) |
| | Copy constructor. More...
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| | DuelingDQN (const int inputDim, const int h1, const int h2, const int outputDim, const bool isNoisy=false, InitType init=InitType(), OutputLayerType outputLayer=OutputLayerType()) |
| | Construct an instance of DuelingDQN class. More...
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| | DuelingDQN (FeatureNetworkType &featureNetwork, AdvantageNetworkType &advantageNetwork, ValueNetworkType &valueNetwork, const bool isNoisy=false) |
| | Construct an instance of DuelingDQN class from a pre-constructed network. More...
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| void | Backward (const arma::mat state, arma::mat &target, arma::mat &gradient) |
| | Perform the backward pass of the state in real batch mode. More...
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| void | Forward (const arma::mat state, arma::mat &actionValue) |
| | Perform the forward pass of the states in real batch mode. More...
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| void | operator= (const DuelingDQN &model) |
| | Copy assignment operator. More...
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| arma::mat & | Parameters () |
| | Modify the Parameters. More...
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| const arma::mat & | Parameters () const |
| | Return the Parameters. More...
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| void | Predict (const arma::mat state, arma::mat &actionValue) |
| | Predict the responses to a given set of predictors. More...
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| void | ResetNoise () |
| | Resets noise of the network, if the network is of type noisy. More...
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| void | ResetParameters () |
| | Resets the parameters of the network. More...
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template<typename OutputLayerType = EmptyLoss<>, typename InitType = GaussianInitialization, typename CompleteNetworkType = FFN<OutputLayerType, InitType>, typename FeatureNetworkType = Sequential<>, typename AdvantageNetworkType = Sequential<>, typename ValueNetworkType = Sequential<>>
class mlpack::rl::DuelingDQN< OutputLayerType, InitType, CompleteNetworkType, FeatureNetworkType, AdvantageNetworkType, ValueNetworkType >
Implementation of the Dueling Deep Q-Learning network.
For more information, see the following.
@misc{wang2015dueling,
author = {Ziyu Wang, Tom Schaul, Matteo Hessel,Hado van Hasselt,
Marc Lanctot, Nando de Freitas},
title = {Dueling Network Architectures for Deep Reinforcement Learning},
year = {2015},
url = {https:
}
- Template Parameters
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| OutputLayerType | The output layer type of the network. |
| InitType | The initialization type used for the network. |
| CompleteNetworkType | The type of network used for full dueling dqn. |
| FeatureNetworkType | The type of network used for feature network. |
| AdvantageNetworkType | The type of network used for advantage network. |
| ValueNetworkType | The type of network used for value network. |
Definition at line 56 of file dueling_dqn.hpp.
| void Predict |
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const arma::mat |
state, |
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arma::mat & |
actionValue |
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) |
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inline |
Predict the responses to a given set of predictors.
The responses will reflect the output of the given output layer as returned by the output layer function.
If you want to pass in a parameter and discard the original parameter object, be sure to use std::move to avoid unnecessary copy.
- Parameters
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| state | Input state. |
| actionValue | Matrix to put output action values of states input. |
Definition at line 187 of file dueling_dqn.hpp.