Uses of Class
net.pakl.rl.ActionSet

Packages that use ActionSet

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 ActionSet in net.pakl.rl

Fields in net.pakl.rl declared as ActionSet

protected ActionSet

Agent.policy

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

java.lang.String	PolicyExtractor.extractOptimalPolicy(ActionSet naivePolicy, ValueFunction valueFunction, World trainedWorld, World testWorld, ReinforcementFunction rf, double discountFactor)
Action	Agent.getBestActionForValueFrom(State s, ValueFunction vf, ActionSet p) This is a function which should probably be called more often to reduce duplicated code.
void	Agent.setPolicy(ActionSet newPolicy)

Uses of ActionSet in net.pakl.rl.maze

Methods in net.pakl.rl.maze that return ActionSet

ActionSet

Toolbox.makeSimpleMazePolicy(MazeWorld mWorld)

Uses of ActionSet in org.eyelanguage.rl.reading

Subclasses of ActionSet in org.eyelanguage.rl.reading

class	ReadingPolicy
class	ReadingPolicyParallel