deer
0.4
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Learning
algorithms
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deer
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A
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B
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C
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D
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E
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F
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G
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I
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L
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M
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N
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O
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P
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Q
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R
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S
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T
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U
A
act() (deer.base_classes.Environment method)
action() (deer.base_classes.Policy method)
(deer.policies.EpsilonGreedyPolicy method)
(deer.policies.LongerExplorationPolicy method)
actions() (deer.agent.DataSet method)
addSample() (deer.agent.DataSet method)
avgBellmanResidual() (deer.agent.NeuralAgent method)
avgEpisodeVValue() (deer.agent.NeuralAgent method)
B
bestAction() (deer.base_classes.Policy method)
C
chooseBestAction() (deer.base_classes.LearningAlgo method)
(deer.learning_algos.AC_net_keras.MyACNetwork method)
(deer.learning_algos.CRAR_keras.CRAR method)
(deer.learning_algos.q_net_keras.MyQNetwork method)
clip_action() (deer.learning_algos.AC_net_keras.MyACNetwork method)
Controller (class in deer.experiment.base_controllers)
CRAR (class in deer.learning_algos.CRAR_keras)
D
DataSet (class in deer.agent)
deer.agent (module)
deer.base_classes.environment (module)
deer.experiment.base_controllers (module)
discountFactor() (deer.agent.NeuralAgent method)
(deer.base_classes.LearningAlgo method)
DiscountFactorController (class in deer.experiment.base_controllers)
dumpNetwork() (deer.agent.NeuralAgent method)
E
end() (deer.base_classes.Environment method)
Environment (class in deer.base_classes)
epsilon() (deer.policies.EpsilonGreedyPolicy method)
(deer.policies.LongerExplorationPolicy method)
EpsilonController (class in deer.experiment.base_controllers)
EpsilonGreedyPolicy (class in deer.policies)
F
FindBestController (class in deer.experiment.base_controllers)
G
getAllParams() (deer.learning_algos.AC_net_keras.MyACNetwork method)
(deer.learning_algos.CRAR_keras.CRAR method)
(deer.learning_algos.q_net_keras.MyQNetwork method)
gradients() (deer.learning_algos.AC_net_keras.MyACNetwork method)
I
inputDimensions() (deer.base_classes.Environment method)
InterleavedTestEpochController (class in deer.experiment.base_controllers)
inTerminalState() (deer.base_classes.Environment method)
L
LearningAlgo (class in deer.base_classes)
learningRate() (deer.agent.NeuralAgent method)
(deer.base_classes.LearningAlgo method)
LearningRateController (class in deer.experiment.base_controllers)
LongerExplorationPolicy (class in deer.policies)
M
MyACNetwork (class in deer.learning_algos.AC_net_keras)
MyQNetwork (class in deer.learning_algos.q_net_keras)
N
nActions() (deer.base_classes.Environment method)
NeuralAgent (class in deer.agent)
O
observations() (deer.agent.DataSet method)
observationType() (deer.base_classes.Environment method)
observe() (deer.base_classes.Environment method)
onActionChosen() (deer.experiment.base_controllers.Controller method)
onActionTaken() (deer.experiment.base_controllers.Controller method)
onEnd() (deer.experiment.base_controllers.Controller method)
onEpisodeEnd() (deer.experiment.base_controllers.Controller method)
onEpochEnd() (deer.experiment.base_controllers.Controller method)
onStart() (deer.experiment.base_controllers.Controller method)
overrideNextAction() (deer.agent.NeuralAgent method)
P
Policy (class in deer.base_classes)
Q
qValues() (deer.base_classes.LearningAlgo method)
(deer.learning_algos.CRAR_keras.CRAR method)
(deer.learning_algos.q_net_keras.MyQNetwork method)
qValues_planning() (deer.learning_algos.CRAR_keras.CRAR method)
qValues_planning_abstr() (deer.learning_algos.CRAR_keras.CRAR method)
R
randomAction() (deer.base_classes.Policy method)
randomBatch() (deer.agent.DataSet method)
randomBatch_nstep() (deer.agent.DataSet method)
reset() (deer.base_classes.Environment method)
rewards() (deer.agent.DataSet method)
run() (deer.agent.NeuralAgent method)
S
setActive() (deer.experiment.base_controllers.Controller method)
setAllParams() (deer.learning_algos.AC_net_keras.MyACNetwork method)
(deer.learning_algos.CRAR_keras.CRAR method)
(deer.learning_algos.q_net_keras.MyQNetwork method)
setControllersActive() (deer.agent.NeuralAgent method)
setDiscountFactor() (deer.agent.NeuralAgent method)
(deer.base_classes.LearningAlgo method)
setEpsilon() (deer.policies.EpsilonGreedyPolicy method)
(deer.policies.LongerExplorationPolicy method)
setLearningRate() (deer.agent.NeuralAgent method)
(deer.base_classes.LearningAlgo method)
(deer.learning_algos.CRAR_keras.CRAR method)
setNetwork() (deer.agent.NeuralAgent method)
summarizePerformance() (deer.base_classes.Environment method)
T
terminals() (deer.agent.DataSet method)
totalRewardOverLastTest() (deer.agent.NeuralAgent method)
train() (deer.agent.NeuralAgent method)
(deer.base_classes.LearningAlgo method)
(deer.learning_algos.AC_net_keras.MyACNetwork method)
(deer.learning_algos.CRAR_keras.CRAR method)
(deer.learning_algos.q_net_keras.MyQNetwork method)
TrainerController (class in deer.experiment.base_controllers)
U
updatePriorities() (deer.agent.DataSet method)
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