rlQAgent

Q-learning reinforcement learning agent

Since R2019a

Description

The Q-learning algorithm is a model-free, online, off-policy reinforcement learning method. A Q-learning agent is a value-based reinforcement learning agent which trains a critic to estimate the return or future rewards.

Note

Q-learning agents do not support recurrent networks.

For more information on Q-learning agents, see Q-Learning Agents.

For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.

Creation

Syntax

agent = rlQAgent(critic,agentOptions)

Description

agent = rlQAgent(critic,agentOptions) creates a Q-learning agent with the specified critic network and sets the AgentOptions property.

Input Arguments

critic — Critic

rlQValueFunction object

Critic, specified as an rlQValueFunction object. For more information on creating critics, see Create Policies and Value Functions.

Properties

AgentOptions — Agent options

rlQAgentOptions object

Agent options, specified as an rlQAgentOptions object.

UseExplorationPolicy — Option to use exploration policy

false (default) | true

Option to use exploration policy when selecting actions, specified as a one of the following logical values.

• false — Use the agent greedy policy when selecting actions.

• true — Use the agent exploration policy when selecting actions.

ObservationInfo — Observation specifications

specification object | array of specification objects

This property is read-only.

Observation specifications, specified as an rlFiniteSetSpec or rlNumericSpec object or an array containing a mix of such objects. Each element in the array defines the properties of an environment observation channel, such as its dimensions, data type, and name.

The value of ObservationInfo matches the corresponding value specified in critic.

ActionInfo — Action specification

rlFiniteSetSpec object

This property is read-only.

Action specifications, specified as an rlFiniteSetSpec object. This object defines the properties of the environment action channel, such as its dimensions, data type, and name.

Note

Only one action channel is allowed.

The value of ActionInfo matches the corresponding value specified in critic.

SampleTime — Sample time of agent

positive scalar | -1

Sample time of agent, specified as a positive scalar or as -1. Setting this parameter to -1 allows for event-based simulations. The value of SampleTime matches the value specified in AgentOptions.

Within a Simulink^® environment, the RL Agent block in which the agent is specified to execute every SampleTime seconds of simulation time. If SampleTime is -1, the block inherits the sample time from its parent subsystem.

Within a MATLAB^® environment, the agent is executed every time the environment advances. In this case, SampleTime is the time interval between consecutive elements in the output experience returned by sim or train. If SampleTime is -1, the time interval between consecutive elements in the returned output experience reflects the timing of the event that triggers the agent execution.

Object Functions

train	Train reinforcement learning agents within a specified environment
sim	Simulate trained reinforcement learning agents within specified environment
getAction	Obtain action from agent, actor, or policy object given environment observations
getActor	Extract actor from reinforcement learning agent
setActor	Set actor of reinforcement learning agent
getCritic	Extract critic from reinforcement learning agent
setCritic	Set critic of reinforcement learning agent
generatePolicyFunction	Generate MATLAB function that evaluates policy of an agent or policy object

Examples

Create a Q-Learning Agent

Create an environment interface. For this example, use the same environment as in the example Train Reinforcement Learning Agent in Basic Grid World.

env = rlPredefinedEnv("BasicGridWorld");

Get observation and action specifications.

obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

Q-learning agents use a parametrized Q-value function to estimate the value of the policy. A Q-value function takes the current observation and an action as inputs and returns a single scalar as output (the estimated discounted cumulative long-term reward for taking the action from the state corresponding to the current observation, and following the policy thereafter).

Since both observation and action spaces are discrete and low-dimensional, use a table to model the Q-value function within the critic. rlTable creates a value table object from the observation and action specifications objects.

Create a table approximation model derived from the environment observation and action specifications.

qTable = rlTable(obsInfo,actInfo);

Create the Q-value function approximator object using qTable and the environment specification objects. For more information, see rlQValueFunction.

critic = rlQValueFunction(qTable,obsInfo,actInfo);

Create a Q-learning agent using the approximator object.

agent = rlQAgent(critic)

agent = 
  rlQAgent with properties:

            AgentOptions: [1x1 rl.option.rlQAgentOptions]
    UseExplorationPolicy: 0
         ObservationInfo: [1x1 rl.util.rlFiniteSetSpec]
              ActionInfo: [1x1 rl.util.rlFiniteSetSpec]
              SampleTime: 1

Specify an Epsilon value of 0.05.

opt = rlSARSAAgentOptions;
agent.AgentOptions.EpsilonGreedyExploration.Epsilon = 0.05;

To check your agent, use getAction to return the action from a random observation.

act = getAction(agent,{randi(numel(obsInfo.Elements))});
act{1}

ans = 1

You can now test and train the agent against the environment.

Version History

Introduced in R2019a