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Packages that use LearningRule | |
connectionRules | |
learningRules | |
models | |
utilities |
Uses of LearningRule in connectionRules |
Classes in connectionRules that implement LearningRule | |
class |
NeighborReinforce
A strategy learning rule that is like reinforcement dynamics. |
Uses of LearningRule in learningRules |
Classes in learningRules that implement LearningRule | |
class |
BgLearning
This is a strategy learning model for the Bala Goyal Model. |
class |
CondorcetLearning
This class is a Condorcet Learning Rule. |
class |
HybridLearning
This class implements the Hybrid learning rule. |
class |
ImitateBestLearning
This is a learning rule where an individual adopts the strategy that did best on the previous round from among those strategies choosen by a neighbor. |
class |
MyopicBRLearning
This class implements myopic best response learning. |
class |
PayoffLearning
A learning rule where individuals learn the payoffs in a n-strategy game. |
class |
SmoothBGLearning
This is a strategy learning model for the Bala Goyal Model. |
class |
SmoothPayoffLearning
A learning rule where individuals learn the payoffs in a n-strategy game. |
class |
SmoothReinforcement
This implements a modification of the standard reinforcement dynamics. |
class |
StratReinforce
Simple reinforcement learning for strategies |
Uses of LearningRule in models |
Methods in models with parameters of type LearningRule | |
void |
StandardModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
StandardModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
Game g,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
SimpleCondorcetModel.createPlayers(Individual[] is,
MutationRule mr,
LearningRule lr,
ConnectionRule cr)
Sets up the individuals |
void |
HybridModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
HybridModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
Game g,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
BgModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
void |
BgModel.createPlayersHigh(Individual[] inds,
int s,
ConnectionRule c,
LearningRule l,
Game g,
double m,
double z)
Forms a new network of specified size; expects random to be set. |
Constructors in models with parameters of type LearningRule | |
RandomNetworkModel(Individual[] i,
Game g,
LearningRule l,
double m,
double p)
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PayoffLearningModel(Individual[] i,
Game g,
ConnectionRule c,
LearningRule l,
double m)
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BiasedNetworkModel(Individual[] i,
Game g,
LearningRule l,
double m,
double min,
double max)
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Uses of LearningRule in utilities |
Methods in utilities that return LearningRule | |
LearningRule |
Individual.getLearningRule()
Returns the learning rule |
Methods in utilities with parameters of type LearningRule | |
void |
Individual.setLearningRule(LearningRule l)
Sets the learning rule for updating strategies |
Constructors in utilities with parameters of type LearningRule | |
Individual(int i,
int s,
MersenneTwister r,
Individual[] nw,
ConnectionRule c,
LearningRule l)
Returns and individual object, but with more initialized. |
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