QLearner

Description:

  A simple reinforcement learning framework that can be used to learn optimal policies for Markov decision processes using Q-learning. Q-learning is a model-free reinforcement learning algorithm that learns an optimal action-value function from experience by repeatedly updating estimates of the Q-value of state-action pairs.

Class Object: QLearner Class.

Inherits from: Object.

matrix

Type: Readonly Field.

Description:

  The matrix that stores state, action, and Q-value.

Signature:

const matrix: {{
		--[[state]] integer,
		--[[action]] integer,
		--[[Q-value]] number
	}}

update

Type: Function.

Description:

  Update Q-value for a state-action pair based on received reward.

Signature:

update: function(self: QLearner, state: integer, action: integer, reward: number)

Parameters:

Parameter	Type	Description
state	integer	Representing the state.
action	integer	Representing the action.
reward	number	Representing the reward received for the action in the state.

getBestAction

Type: Function.

Description:

  Returns the best action for a given state based on the current Q-values.

Signature:

getBestAction: function(self: QLearner, state: integer): integer

Parameters:

Parameter	Type	Description
state	integer	The current state.

Returns:

Return Type	Description
integer	The action with the highest Q-value for the given state.

load

Type: Function.

Description:

  Load Q-values from a matrix of state-action pairs.

Signature:

load: function(self: QLearner, values: {{
			--[[state]] integer,
			--[[action]] integer,
			--[[Q-value]] number
		}})

Parameters:

Parameter	Type	Description
values	{{integer - The state, integer The action, number - The Q-value for the given state-action pair}}	The matrix of state-action pairs to load.