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Class sim.mdp.LQR 

java.lang.Object
   |
   +----sim.mdp.MDP
           |
           +----sim.mdp.LQR
public class LQR
extends MDP
A Markov Decision Process that takes a state and action and returns a new state and a reinforcement. This MDP is deterministic. The state space is the interval [-1,1], discretized by the time step dt. An agent sits on one of the discrete states defined by dt. The agent can perform 2 possible actions: go left (-1), and go right (1). The reinforcement returned after performing an action is the new position on the number line squared. The object of this MDP is to minimize total discounted reinforcement. If the parameter discrete is not set to true, then epochSize should be set if the experiment is using epochwise training. This might be the case if, for example, an inherently epochwise method such as conjugate gradient is the learning algorithm. In that case, the incremental parameter for the experiment, assuming one exists, would be set to false. However, the discrete parameter of LQR could be set to false and a number of gradients to average over would be defined using epochSize. If discrete is set to false then epochSize is ignored.

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:
1.28, 22 Aug 97
Author:
Mance Harmon

Constructor Index

o LQR()

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 action 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 a 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 LQR 
 public LQR()

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. dt must evenly divide 2.

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 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. If the state passed in is 1 then the next state is -1.

Throws: MatrixException
Vector is 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 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 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 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. This doesn't generate random states with uniform probability. States -1 and 1 are half as likely as the other states.

Throws: MatrixException
Vector is 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 a 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. Most MDPs will generally make this a constant, given in the parsed string.

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. This corrupts the original action passed in by returning in its place the best action for the given state.

Throws: MatrixException
column vectors are wrong size or shape
Overrides:
findValAct in class MDP
o findValue 
 public double findValue(Matrix state,
                         Matrix optAction,
                         PDouble gamma,
                         FunApp f,
                         PDouble dt,
                         Matrix outputs,
                         PDouble reinforcement,
                         PBoolean valueKnown,
                         NumExp explorationFactor,
                         Random random) throws MatrixException
Find the max over action for where V(x') is the value of the successor state given state x, R is the reinforcement, gamma is the discount factor. This method is used in the object ValIter (value iteration).

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