Reinforcement Learning with R

Act.A3C | Determines which action the algorithm takes |

Action.2.Choice.PD | Action to Array for Prisoners Dilemma |

Action.Encoding.Info.PD | Get Info of Action Encoding |

Action.Encoding.Info.Simple.Game | Get Info of Action Encoding |

Act.Qlearning | Determines which action to take |

Act.Qlearning.old | Determines which action to take |

Act.QLearningPers | Determines which action to take |

Act.QLearning.Surprise | Determines which action to take |

Act.QPathing | Determines which action to take |

Act.QPredictions | Determines which action to take |

Advantage.function | Calculates N-Step Returns or weighted Temporal Difference... |

Alphabet3 | A student strategy |

Antitiktak1 | A student strategy |

a.tadaaa.1 | A student strategy |

Calc.Endstate.Value.QPredictions | Calculates Endstate value |

Calc.Reward.QPathing | Calc.Reward.QPathing |

Calc.Reward.QPredictions | Calc.Reward.QPredictions |

Calc.Reward.QPredictions.expectedQ | Calculate Expected Value based on action |

Calc.Reward.QPredictions.expectedReward | Calculate Expected immediate Reward based on action |

Choice.2.Action.PD | Array to Action for Prisoners Dilemma |

Choice.2.Action.Simple.Game | Array to Action for Simple Game |

Convert.2.train | Converts stored Memory into arrays. |

Define_Graph | Graph for Network Loss according to A3C. |

Define_Graph_Gradient_Update | Graph to update Network weights |

Encode.Game.States.PD | Transforms List of Gamestates to std encoding form |

Extend.Memory.Qlearning | Extend Memory by specified experiences |

Extend.Memory.Qlearning.old | Extend Memory by specified experiences |

Extend.Memory.QLearningPers | Extend Memory by specified experiences |

Extend.Memory.QLearning.Surprise | Extend Memory by specified experiences |

Extend.Memory.QPathing | Extend Memory by specified experiences |

Extend.Memory.QPredictions | Extend Memory by specified experiences |

false.friend | A student strategy |

fix.price.loc | Example srategy for the Hotelling game |

Generate.Start.State.PD | Generates Start State for Prisoners Dilemma Game |

Generate.Start.State.Simple.Game | Generates Start State for Simple Game |

Get.Def.Par.A3C | Get Default Parameters of A3C. |

Get.Def.Par.Neural.Network | Define default Parameters of the Neural Network Function |

Get.Def.Par.Neural.Network.A3C | Get Default Parameters of the Feed-Forward Neural Network for... |

Get.Def.Par.Neural.Network.A3C.LSTM | Get Default Parameters of the LSTM Neural Network for the A3C... |

Get.Def.Par.QLearning | Delivers some default Parameters of Q-learning |

Get.Def.Par.QLearning.old | Delivers some default Parameters of Q-learning |

Get.Def.Par.QLearningPers | Delivers some default Parameters of Q-learning |

Get.Def.Par.QLearning.Surprise | Delivers some default Parameters of Q-learning |

Get.Def.Par.QPathing | Delivers some default Parameters of Q-learning |

Get.Def.Par.QPredictions | Delivers some default Parameters of Q-Predictions |

Get.Def.Par.RNN | Define default Parameters of the RNN Function |

Get.Game.Object.PD | Get Game Object which fully defines Prisoners Dilemma. |

Get.Game.Object.Simple.Game | Get Game Object which fully defines simple game. |

Get.Game.Param.PD | Standard Parameters of Repeated Prisoners Dilemma Returns a... |

Get.Par.PD | Defines model parameters for 'Prisoners Dilemma' |

Get.Par.Simple.Game | Defines model parameters for 'Simple Game' |

getrich | A student strategy |

Globaler.Tit.4.Tat | A student strategy |

Hybrid.Predict.Action.Values.QPathing | Generates best guesses based on Experience |

Hybrid.Predict.Action.Values.QPredictions | Generates best guesses based on Experience |

Initialise.A3C | Set changeable A3C Parameters. |

Initialise.Qlearning | Set changeable model variables |

Initialise.Qlearning.old | Set changeable model variables |

Initialise.QLearningPers | Set changeable model variables |

Initialise.QLearning.Surprise | Set changeable model variables |

Initialise.QPathing | Set changeable model variables |

Initialise.QPredictions | Set changeable model variables |

into.spaaaace | A grad student strategy |

meineStrat2 | A student strategy |

Memory.Random.Play.PD | Generate Memory where strategies play against a random... |

Memory.Self.Play.PD | Generate Memory where strategies play against themselves |

Model.strat.maximum.full.Ten | A strategy to be used after model has been trained |

my.antistrat2 | A student strategy Answers strat2 [0.566 after 1000 Rounds] |

nashtag1 | A student strategy |

NN.strat.full.zero | A strategy to be used after model has been trained |

NN.strat.main | The actual strategy after model has been trained |

NN.strat.Slim.TenTen | A strategy to be used after model has been trained |

NN.strat.Slim.TenTen.QLearning | A strategy to be used after model has been trained |

NN.strat.static.end.Ten | A strategy to be used after model has been trained |

nottitfortat | A student strategy |

phases | A student strategy |

Play.Multiple.Games.QLearningPers | Train multiple games |

Play.On.Strategy.QLearningPers | Play the game based on strategy |

Predict.Neural.Network | Evaluate Neural Network |

Predict.Neural.Network.A3C | Predict Neural Network |

Predict.RNN | Evaluate Recurrent RNN |

prep.data.4.shiny | Prepare Worker Memory to visualize with shiny |

prof.strat | A student strategy |

pudb.strat2 | A student strategy |

Q.on.hist.PD.QLearning | Q-values based on history of IPD |

Q.on.hist.PD.QLearning.Surprise | Q-values based on history of IPD |

rainbow.unicorn.antistrat2 | A student strategy |

redim.state | Change dimensionality of the state array. |

Replay.Qlearning | Train model of Q learning |

Replay.Qlearning.old | Train model of Q learning |

Replay.QLearningPers | Train model of Q learning |

Replay.QLearning.Surprise | Train model of Q learning |

Replay.QPathing | Train model of Q Pathing |

Replay.QPredictions | Train model of Q Pathing |

schachmatt_tournament | A student strategy |

screams.in.space | A grad student strategy |

seda.strat2 | A student strategy |

Setup.Neural.Network | Setup a Neural Network |

Setup.Neural.Network.A3C | Setup a Feed-Forward Neural Network for the... |

Setup.Neural.Network.A3C.LSTM | Setup a Neural Network with an LSTM-Layer for the... |

Setup.QLearning | Sets up a model based on model parameters |

Setup.QLearning.old | Sets up a model based on model parameters |

Setup.QLearningPers | Sets up a model based on model parameters |

Setup.QLearning.Surprise | Sets up a model based on model parameters |

Setup.QPathing | Q-Pathing is rather similar to Q-learning but we have... |

Setup.QPredictions | Q-Predictions is rather similar to Q-learning but we use a... |

Setup.RNN | Setup a RNN |

squishy.the.octopus | A student strategy |

State.2.Array.PD | State to Array for Prisoners Dilemma |

State.2.Array.Simple.Game | State to Array for Simple Game |

State.Transition.PD | Get next State of Prisoners Dilemma Game |

State.Transition.Simple.Game | Get next State of Simple Game |

strat1 | A student strategy |

strat2 | A student strategy |

strat3 | A student strategy |

strat4 | A student strategy |

stratego | A student strategy |

ta.daaa | A student strategy |

TikTak1 | A student strategy |

Train.A3c | Use the A3C algorithm to train a model |

Train.Neural.Network | Train Neural Network |

Train.On.Memory.QLearning | Trains model based on memory |

Train.On.Memory.QLearningPers | Trains model based on memory |

Train.On.Memory.QLearning.Surprise | Trains model based on memory |

Train.QLearning | Train a model based on Q-Learning |

Train.QLearning.old | Train a model based on Q-Learning |

Train.QLearningPers | Train a model based on Q-Learning |

Train.QLearning.Surprise | Train a model based on Q-Learning |

Train.QPathing | Train a model based on Q-Learning |

Train.QPredictions | Train a model based on Q-Learning |

Train.RNN | Train RNN |

traveling.salesman | Example srategy for the Hotelling game |

Update.Evaluator.QLearningPers | Controlled Copying of Models |

Update.Memory.QLearning | Add historic Q-Values to memory |

Update.Memory.QLearningPers | Add historic Q-Values to memory |

Update.Memory.QLearning.Surprise | Add historic Q-Values to memory |

Update.Net.QPathing | Internal Function |

Update.Net.QPredictions | Internal Function |

viva.PD.Strategy | A student strategy |

Worker.A3C | Defines an Agent based on the A3C-Algorithm |

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