Uses of Package
net.pakl.rl

Packages that use net.pakl.rl

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.analysis	Tools to extract data from the adaptive reading agent's logfile for later processing.
org.eyelanguage.rl.reading	Code for the Adaptive Reading Agent; see ReadingMain for parameters and default values.

Classes in net.pakl.rl used by net.pakl.rl

Action
A basic element of our Reinforcement Learning simulation framework; Actions are returned by a ActionSet, and when performed by an Agent, moves it to another state in a World

ActionSet
A ActionSet maps States to possible Actions and is used by an Agent in considering its possible next moves.

Agent
This class represents an agent, which is capable of iterating over all of the actions from its given state given a ActionSet.

HasVectorRepresentation
Implemented by state objects that are expected to be used with neural network value functions.

ReinforcementFunction
This class contains all of the required functionality for a reward function and should be applicable to all worlds.

State
A State represents a position or attitude in a World (that is, physical and/or mental attitudes); from any State, some Action is possible.

ValueFunction
A ValueFunction maps states from a World, to values, and may be implemented with a neural network or a HashMap.

ValueFunctionHashMap
A ValueFunction maps states, which are positions in a World, to values, and may be replaced with a neural network.

ValueFunctionPerceptron

ValueFunctionResidualAlgorithmPerceptron

World
Basic specification of a world, which ensures that all worlds have certain methods (functions) and can be built, traversed and can have a physical distance measure defined.

Classes in net.pakl.rl used by net.pakl.rl.maze

Action
A basic element of our Reinforcement Learning simulation framework; Actions are returned by a ActionSet, and when performed by an Agent, moves it to another state in a World

ActionSet
A ActionSet maps States to possible Actions and is used by an Agent in considering its possible next moves.

HasVectorRepresentation
Implemented by state objects that are expected to be used with neural network value functions.

State
A State represents a position or attitude in a World (that is, physical and/or mental attitudes); from any State, some Action is possible.

World
Basic specification of a world, which ensures that all worlds have certain methods (functions) and can be built, traversed and can have a physical distance measure defined.

Classes in net.pakl.rl used by org.eyelanguage.rl.analysis

ValueFunctionHashMap
A ValueFunction maps states, which are positions in a World, to values, and may be replaced with a neural network.

ValueFunctionPerceptron

Classes in net.pakl.rl used by org.eyelanguage.rl.reading

Action
A basic element of our Reinforcement Learning simulation framework; Actions are returned by a ActionSet, and when performed by an Agent, moves it to another state in a World

ActionSet
A ActionSet maps States to possible Actions and is used by an Agent in considering its possible next moves.

HasVectorRepresentation
Implemented by state objects that are expected to be used with neural network value functions.

ReinforcementFunction
This class contains all of the required functionality for a reward function and should be applicable to all worlds.

State
A State represents a position or attitude in a World (that is, physical and/or mental attitudes); from any State, some Action is possible.

SubdivisionIdentification
Allows a state to identify the patch to which it belongs in case an agent needs to retrieve the value of this state; typically the name of the patch is the name of the value function that needs to be loaded by the agent.

ValueFunction
A ValueFunction maps states from a World, to values, and may be implemented with a neural network or a HashMap.

World
Basic specification of a world, which ensures that all worlds have certain methods (functions) and can be built, traversed and can have a physical distance measure defined.