Uses of Interface
net.pakl.rl.Action

Packages that use Action

net.pakl.rl	These are the basic reinforcement learning model classes -- every reinforcement learning problem can be described (minimally) as a World (collection of states), Policy, ValueFunction, and Actions.
net.pakl.rl.maze	World and policy for a 2D problem with impassable obstacles.
org.eyelanguage.rl.reading	Code for the Adaptive Reading Agent; see ReadingMain for parameters and default values.

Uses of Action in net.pakl.rl

Methods in net.pakl.rl that return Action

Action	Agent.getBestActionForValueFrom(State s, ValueFunction vf, ActionSet p) This is a function which should probably be called more often to reduce duplicated code.
Action	ActionSet.getRandomAllowedAction(State state)

Methods in net.pakl.rl with parameters of type Action

void	ActionSet.allowAction(State state, Action newAction) This method allows you to program in a possible action given a state.
State	World.getNewState(State oldState, Action action) Given a state and an action, returns the resulting state.
double	ReinforcementFunction.getReward(State state, Action action) Return the reward given an action from a particular state.
double	ReinforcementFunction.getReward(State state, Action action, State newState) Sometimes a reward is also based on the resulting state.
void	ActionSet.removeAction(State state, Action oldAction) This method allows you to deprogram an action from being possible
void	ReinforcementFunction.setReward(State state, Action action, double newReward)

Constructors in net.pakl.rl with parameters of type Action

StateActionPair(State s, Action a)

Uses of Action in net.pakl.rl.maze

Classes in net.pakl.rl.maze that implement Action

class

Action2D This class represents a one-dimensional action, which could correspond to a movement vector in a 2D World such as MazeWorld

Methods in net.pakl.rl.maze with parameters of type Action

State

MazeWorld.getNewState(State oldState, Action action) This critical method returns the new state given an action from an old state, and in this case, simply adds state to position to simulate movement.

Uses of Action in org.eyelanguage.rl.reading

Classes in org.eyelanguage.rl.reading that implement Action

class

ReadingAction

Methods in org.eyelanguage.rl.reading with parameters of type Action

State	SentenceWorldParallel.getNewState(State oldGenericState, Action genericAction) Call the same code that is in the serial SentenceWorld newState(state, action), but also calls a new function called SentenceWorldParallel.performParallelLexicalProcessing(org.eyelanguage.rl.reading.ReadingStateParallelRelative) on the state.
State	SentenceWorld.getNewState(State oldGenericState, Action genericAction) This critical method returns the new state given an action from an old state, and in this case, simply adds state to position to simulate movement.
State	SentenceWorld.getNewState(State oldGenericState, Action genericAction, java.lang.String r)
double	ReadingReinforcementFunction.getReward(State state, Action action)
double	ReadingParallelReinforcementFunction.getReward(State state, Action action)
double	ReadingReinforcementFunction.getReward(State state, Action action, State resultingState)
double	ReadingParallelReinforcementFunction.getReward(State state, Action action, State resultingState)