Index

A | B | C | D | E | F | I | L | M | N | O | Q | R | S | T | U

A

act() (deer.base_classes.Environment method)
actions() (deer.agent.DataSet method)
addSample() (deer.agent.DataSet method)
avgBellmanResidual() (deer.agent.NeuralAgent method)
avgEpisodeVValue() (deer.agent.NeuralAgent method)

B

bestAction() (deer.agent.NeuralAgent method)

C

chooseBestAction() (deer.base_classes.QNetwork method)
(deer.q_networks.q_net_theano.MyQNetwork method)
Controller (class in deer.experiment.base_controllers)

D

DataSet (class in deer.agent)
deer.agent (module)
deer.base_classes.Environment (module)
deer.base_classes.QNetwork (module)
deer.experiment.base_controllers (module)
discountFactor() (deer.agent.NeuralAgent method)
(deer.base_classes.QNetwork method)
DiscountFactorController (class in deer.experiment.base_controllers)

E

Environment (class in deer.base_classes)
epsilon() (deer.agent.NeuralAgent method)
EpsilonController (class in deer.experiment.base_controllers)

F

FindBestController (class in deer.experiment.base_controllers)

I

inputDimensions() (deer.base_classes.Environment method)
InterleavedTestEpochController (class in deer.experiment.base_controllers)
inTerminalState() (deer.base_classes.Environment method)

L

learningRate() (deer.agent.NeuralAgent method)
(deer.base_classes.QNetwork method)
LearningRateController (class in deer.experiment.base_controllers)

M

MyQNetwork (class in deer.q_networks.q_net_theano)

N

nActions() (deer.base_classes.Environment method)
nElems() (deer.agent.DataSet 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)

Q

QNetwork (class in deer.base_classes)
qValues() (deer.base_classes.QNetwork method)
(deer.q_networks.q_net_theano.MyQNetwork method)

R

randomBatch() (deer.agent.DataSet method)
reset() (deer.base_classes.Environment method)
rewards() (deer.agent.DataSet method)

S

setActive() (deer.experiment.base_controllers.Controller method)
setControllersActive() (deer.agent.NeuralAgent method)
setDiscountFactor() (deer.agent.NeuralAgent method)
(deer.base_classes.QNetwork method)
setEpsilon() (deer.agent.NeuralAgent method)
setLearningRate() (deer.agent.NeuralAgent method)
(deer.base_classes.QNetwork method)
summarizePerformance() (deer.base_classes.Environment method)

T

terminals() (deer.agent.DataSet method)
totalRewardOverLastTest() (deer.agent.NeuralAgent method)
train() (deer.base_classes.QNetwork method)
(deer.q_networks.q_net_theano.MyQNetwork method)
TrainerController (class in deer.experiment.base_controllers)

U

update_priorities() (deer.agent.DataSet method)