public class NPTRothErevLearner extends RothErevLearner
A modification of RothErev to address parameter degeneracy, and modified learning with 0-reward. These modifications are made in the context of using the RE algorithm for trader agents in a double auction. See:
"Market Power and Efficiency in a Computational Electricity Market with
Discriminatory Double-Auction Pricing" Nicolaisen, Petrov & Tesfatsion
in IEEE Transactions on Evolutionary Computation Vol. 5, No. 5, p 504.
deltaP, e, iteration, k, lastAction, probabilities, q, r, s1
monitor
Constructor and Description |
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NPTRothErevLearner(int k,
double r,
double e,
double s1,
cern.jet.random.engine.RandomEngine prng) |
NPTRothErevLearner(int k,
cern.jet.random.engine.RandomEngine prng) |
NPTRothErevLearner(int k,
cern.jet.random.engine.RandomEngine prng,
double[] propensities) |
NPTRothErevLearner(cern.jet.random.engine.RandomEngine prng) |
Modifier and Type | Method and Description |
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double |
experience(int i,
int action,
double reward)
The modified update function.
|
act, bestAction, choose, dumpState, getE, getIteration, getK, getLastAction, getLearningDelta, getNumberOfActions, getProbabilities, getProbability, getR, getS1, protoClone, resetPropensities, reward, setExperimentation, setPropensities, setRecency, setScaling, toString, updateProbabilities, updatePropensities, validateParams, worstAction
monitor
public NPTRothErevLearner(int k, double r, double e, double s1, cern.jet.random.engine.RandomEngine prng)
public NPTRothErevLearner(cern.jet.random.engine.RandomEngine prng)
public NPTRothErevLearner(int k, cern.jet.random.engine.RandomEngine prng, double[] propensities)
public NPTRothErevLearner(int k, cern.jet.random.engine.RandomEngine prng)
public double experience(int i, int action, double reward)
experience
in class RothErevLearner
i
- The action under considerationaction
- The last action chosenCopyright © 2014. All rights reserved.