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Class sim.mdp.Hall 

java.lang.Object
   |
   +----sim.mdp.MDP
           |
           +----sim.mdp.Hall
public class Hall
extends MDP
A Markov Chain that takes a state and returns a new state and a reinforcement. If the next state is fed back in as the state, it can run a simulation. If the state is repeatedly randomized, it can be used for learning with random transitions. The chain has boundaries of [-1,1]. The number of states is dependent upon dt. The reinforcement on each transition is 1 and state 1 is an absorbing state with a value of 0.

This code is (c) 1996 Mance E. Harmon <harmonme@aa.wpafb.af.mil>, http://www.aa.wpafb.af.mil/~harmonme
The source and object code may be redistributed freely provided no fee is charged. If the code is modified, please state so in the comments.

Version:
1.07, 22 Aug 97
Author:
Mance Harmon

Constructor Index

o Hall()

Method Index

o actionSize()
Return the number of elements in the action vector.
o findValAct(Matrix, Matrix, FunApp, Matrix, PBoolean)
Find the value and best action of this state.
o findValue(Matrix, Matrix, PDouble, FunApp, PDouble, Matrix, PDouble, PBoolean, NumExp, Random)
Find the max over actions for where V(x') is the value of the successor state given state x, R is the reinforcement, gamma is the discount factor.
o getAction(Matrix, Matrix, Random)
Return the next possible action in a state given an action.
o getParameters(int)
Return a parameter array if BNF(), parse(), and unparse() are to be automated, null otherwise.
o getState(Matrix, PDouble, Random)
Return the next state when doing epoch-wise training.
o initialAction(Matrix, Matrix, Random)
Return an initial action possible in a given state.
o initialState(Matrix, Random)
Return a start state for epoch-wise training.
o nextState(Matrix, Matrix, Matrix, PDouble, PBoolean, Random)
Find the next state given a state and action, and return the reinforcement received.
o numActions(Matrix)
Return the number of actions in each state.
o numPairs(PDouble)
Return the number of state/action pairs for a given dt.
o numStates(PDouble)
Return the number of states in this LQR for a given dt.
o randomAction(Matrix, Matrix, Random)
Generates a random action from those possible.
o randomState(Matrix, Random)
Generates a random state from those possible.
o stateSize()
Return the number of elements in the state vector.

Constructors

o Hall 
 public Hall()

Methods

o getParameters 
 public Object[][] getParameters(int lang)
Return a parameter array if BNF(), parse(), and unparse() are to be automated, null otherwise.

Overrides:
getParameters in class MDP
See Also:
getParameters
o numStates 
 public int numStates(PDouble dt)
Return the number of states in this LQR for a given dt. The state space is the number line from [-1,1] and is discretized in units of dt.

Overrides:
numStates in class MDP
o stateSize 
 public int stateSize()
Return the number of elements in the state vector.

Overrides:
stateSize in class MDP
o initialState 
 public void initialState(Matrix state,
                          Random random) throws MatrixException
Return a start state for epoch-wise training.

Throws: MatrixException
Vector is the wrong length.
Overrides:
initialState in class MDP
o getState 
 public void getState(Matrix state,
                      PDouble dt,
                      Random random) throws MatrixException
Return the next state when doing epoch-wise training. This functions as a circular queue.

Throws: MatrixException
Vector is the wrong length.
Overrides:
getState in class MDP
o actionSize 
 public int actionSize()
Return the number of elements in the action vector.

Overrides:
actionSize in class MDP
o numActions 
 public int numActions(Matrix state)
Return the number of actions in each state.

Overrides:
numActions in class MDP
o initialAction 
 public void initialAction(Matrix state,
                           Matrix action,
                           Random random) throws MatrixException
Return an initial action possible in a given state.

Throws: MatrixException
Vector is the wrong length.
Overrides:
initialAction in class MDP
o getAction 
 public void getAction(Matrix state,
                       Matrix action,
                       Random random) throws MatrixException
Return the next possible action in a state given an action.

Throws: MatrixException
Vector is the wrong length.
Overrides:
getAction in class MDP
o numPairs 
 public int numPairs(PDouble dt)
Return the number of state/action pairs for a given dt. This only works for dt's in which 2 is evenly divisible by dt.

Overrides:
numPairs in class MDP
o randomAction 
 public void randomAction(Matrix state,
                          Matrix action,
                          Random random) throws MatrixException
Generates a random action from those possible.

Throws: MatrixException
Vector is the wrong length.
Overrides:
randomAction in class MDP
o randomState 
 public void randomState(Matrix state,
                         Random random) throws MatrixException
Generates a random state from those possible.

Throws: MatrixException
Vector is the wrong length.
Overrides:
randomState in class MDP
o nextState 
 public double nextState(Matrix state,
                         Matrix action,
                         Matrix newState,
                         PDouble dt,
                         PBoolean valueKnown,
                         Random random) throws MatrixException
Find the next state given a state and action, and return the reinforcement received. All 3 should be vectors (single-column matrices). The duration of the time step, dt, is also returned.

Throws: MatrixException
if sizes aren't right.
Overrides:
nextState in class MDP
o findValAct 
 public double findValAct(Matrix state,
                          Matrix action,
                          FunApp f,
                          Matrix outputs,
                          PBoolean valueKnown) throws MatrixException
Find the value and best action of this state.

Throws: MatrixException
column vectors are wrong size or shape
Overrides:
findValAct in class MDP
o findValue 
 public double findValue(Matrix state,
                         Matrix action,
                         PDouble gamma,
                         FunApp f,
                         PDouble dt,
                         Matrix outputs,
                         PDouble reinforcement,
                         PBoolean valueKnown,
                         NumExp explorationFactor,
                         Random random) throws MatrixException
Find the max over actions for where V(x') is the value of the successor state given state x, R is the reinforcement, gamma is the discount factor. Return This method is used in the object ValIteration (value iteration). The last performed in this method should be done for the successor state associate with the optimal action. In this case there is only one action.

Throws: MatrixException
column vectors are wrong size or shape
Overrides:
findValue in class MDP 
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