rlQAgent

Q-learning reinforcement learning agent

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.

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 network representation

rlQValueRepresentation object

Critic network representation, specified as an rlQValueRepresentation object. For more information on creating critic representations, see Create Policy and Value Function Representations.

Properties

AgentOptions — Agent options

rlQAgentOptions object

Agent options, specified as an rlQAgentOptions object.

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 or actor representation given environment observations
getActor	Get actor representation from reinforcement learning agent
setActor	Set actor representation of reinforcement learning agent
getCritic	Get critic representation from reinforcement learning agent
setCritic	Set critic representation of reinforcement learning agent
generatePolicyFunction	Create function that evaluates trained policy of reinforcement learning agent

Examples

Create a Q-Learning Agent

Create an environment interface.

env = rlPredefinedEnv("BasicGridWorld");

Create a critic Q-value function representation using a Q-table derived from the environment observation and action specifications.

qTable = rlTable(getObservationInfo(env),getActionInfo(env));
critic = rlQValueRepresentation(qTable,getObservationInfo(env),getActionInfo(env));

Create a Q-learning agent using the specified critic value function and an epsilon value of 0.05.

opt = rlQAgentOptions;
opt.EpsilonGreedyExploration.Epsilon = 0.05;

agent = rlQAgent(critic,opt)

agent = 
  rlQAgent with properties:

    AgentOptions: [1x1 rl.option.rlQAgentOptions]

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

getAction(agent,{randi(25)})

ans = 1

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