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Class EDU.gatech.cc.is.learning.i_AverageLearner_id
java.lang.Object
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+----EDU.gatech.cc.is.learning.i_ReinforcementLearner_id
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+----EDU.gatech.cc.is.learning.i_AverageLearner_id
- public class i_AverageLearner_id
- extends i_ReinforcementLearner_id
- implements Cloneable, Serializable
An object that learns to select from several actions based on
a reward. Uses the Q-learning method but assuming average rewards.
The module will learn to select a discrete output based on
state and a continuous reinforcement input. The "i"s in front
of and behind the name imply that this class takes integers as
input and output. The "d" indicates a double for the reinforcement
input (i.e. a continuous value).
Copyright
(c)1997 Georgia Tech Research Corporation
- Version:
- $Revision: 1.3 $
- Author:
- Tucker Balch (tucker@cc.gatech.edu)
-
i_AverageLearner_id(int, int)
- Instantiate a Q learner using default parameters.
-
endTrial(double, double)
- Called when the current trial ends.
-
initTrial(int)
- Called to initialize for a new trial.
-
query(int, double)
- Select an output based on the state and reward.
-
readPolicy()
- Read the policy from a file.
-
savePolicy()
- Write the policy to a file.
-
setRandomRate(double)
- Set the random rate for the Average-learner.
-
setRandomRateDecay(double)
- Set the random decay for the Average-learner.
-
toString()
- Generate a String that describes the current state of the
learner.
i_AverageLearner_id
public i_AverageLearner_id(int numstatesin,
int numactionsin)
- Instantiate a Q learner using default parameters.
Parameters may be adjusted using accessor methods.
- Parameters:
- numstates - int, the number of states the system could be in.
- numactions - int, the number of actions or outputs to
select from.
setRandomRate
public void setRandomRate(double r)
- Set the random rate for the Average-learner.
This reflects how frequently it picks a random action.
Should be between 0 and 1.
- Parameters:
- r - double, the new value for random rate (0 < r < 1).
setRandomRateDecay
public void setRandomRateDecay(double r)
- Set the random decay for the Average-learner.
This reflects how quickly the rate of chosing random actions
decays. 1 would never decay, 0 would cause it to immediately
quit chosing random values.
Should be between 0 and 1.
- Parameters:
- r - double, the new value for randomdecay (0 < r < 1).
toString
public String toString()
- Generate a String that describes the current state of the
learner.
- Returns:
- a String describing the learner.
- Overrides:
- toString in class i_ReinforcementLearner_id
query
public int query(int yn,
double rn)
- Select an output based on the state and reward.
- Parameters:
- statein - int, the current state.
- rewardin - double, reward for the last output, positive
numbers are "good."
- Overrides:
- query in class i_ReinforcementLearner_id
endTrial
public void endTrial(double Vn,
double rn)
- Called when the current trial ends.
- Parameters:
- Vn - double, the value of the absorbing state.
- reward - double, the reward for the last output.
- Overrides:
- endTrial in class i_ReinforcementLearner_id
initTrial
public int initTrial(int s)
- Called to initialize for a new trial.
- Overrides:
- initTrial in class i_ReinforcementLearner_id
readPolicy
public void readPolicy() throws IOException
- Read the policy from a file.
- Parameters:
- filename - String, the name of the file to read from.
- Overrides:
- readPolicy in class i_ReinforcementLearner_id
savePolicy
public void savePolicy() throws IOException
- Write the policy to a file.
- Parameters:
- filename - String, the name of the file to write to.
- Overrides:
- savePolicy in class i_ReinforcementLearner_id
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