rlDQNAgent

Deep Q-network (DQN) reinforcement learning agent

Since R2019a

Description

The deep Q-network (DQN) algorithm is a model-free, online, off-policy, discrete action-space reinforcement learning method. A DQN agent is a value-based reinforcement learning agent that trains a critic to estimate the expected discounted cumulative long-term reward when following the optimal policy. DQN is a variant of Q-learning.

For more information, Deep Q-Network (DQN) Agents. For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.

Creation

Syntax

agent = rlDQNAgent(observationInfo,actionInfo)

agent = rlDQNAgent(observationInfo,actionInfo,initOpts)

agent = rlDQNAgent(critic)

agent = rlDQNAgent(critic,agentOptions)

Description

Create Agent from Observation and Action Specifications

agent = rlDQNAgent(observationInfo,actionInfo) creates a DQN agent for an environment with the given observation and action specifications, using default initialization options. The critic in the agent uses a default vector (that is, multi-output) Q-value deep neural network built from the observation specification observationInfo and the action specification actionInfo. The ObservationInfo and ActionInfo properties of agent are set to the observationInfo and actionInfo input arguments, respectively.

agent = rlDQNAgent(observationInfo,actionInfo,initOpts) creates a DQN agent for an environment with the given observation and action specifications. The agent uses a default network configured using options specified in the initOpts object. For more information on the initialization options, see rlAgentInitializationOptions.

Create Agent from Critic

agent = rlDQNAgent(critic) creates a DQN agent with the specified critic network using a default option set for a DQN agent.

Specify Agent Options

agent = rlDQNAgent(critic,agentOptions) creates a DQN agent with the specified critic network and sets the AgentOptions property to the agentOptions input argument. Use this syntax after any of the input arguments in the previous syntaxes..

Input Arguments

initOpts — Agent initialization options

rlAgentInitializationOptions object

Agent initialization options, specified as an rlAgentInitializationOptions object.

critic — Critic

rlQValueFunction object | rlVectorQValueFunction object

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

Your critic can use a recurrent neural network as its function approximator. However, only rlVectorQValueFunction supports recurrent neural networks. For an example, see Create DQN Agent with Recurrent Neural Network.

Properties

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.

If you create the agent by specifying the critic, the value of ObservationInfo matches the corresponding value specified in critic.

You can extract observationInfo from an existing environment or agent using getObservationInfo. You can also construct the specifications manually using rlFiniteSetSpec or rlNumericSpec.

ActionInfo — Action specification

rlFiniteSetSpec object

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.

If you create the agent by specifying a critic object, the value of ActionInfo matches the value specified in critic.

You can extract actionInfo from an existing environment or agent using getActionInfo. You can also construct the specification manually using rlFiniteSetSpec.

AgentOptions — Agent options

rlDQNAgentOptions object

Agent options, specified as an rlDQNAgentOptions object.

If you create a DQN agent with a default critic that uses a recurrent neural network, the default value of AgentOptions.SequenceLength is 32.

ExperienceBuffer — Experience buffer

rlReplayMemory object | rlPrioritizedReplayMemory object | rlHindsightReplayMemory object | rlHindsightPrioritizedReplayMemory object

Experience buffer, specified as one of the following replay memory objects.

• rlReplayMemory (default)

• rlPrioritizedReplayMemory

• rlHindsightReplayMemory

• rlHindsightPrioritizedReplayMemory

Note

Agents with recursive neural networks only support rlReplayMemory and rlHindsightReplayMemory buffers.

During training the agent stores each of its experiences (S,A,R,S',D) in the buffer. Here:

• S is the current observation of the environment.

• A is the action taken by the agent.

• R is the reward for taking action A.

• S' is the next observation after taking action A.

• D is the is-done signal after taking action A.

The agent then samples mini-batches of experiences from the buffer and uses these mini-batches to update its actor and critic function approximators.

UseExplorationPolicy — Option to use exploration policy for simulation and deployment

true (default) | false

Option to use exploration policy when selecting actions during simulation or after deployment, specified as a one of the following logical values.

• true — Use the base agent exploration policy when selecting actions in sim and generatePolicyFunction. Specifically, in this case the agent uses the rlEpsilonGreedyPolicy. Since the action selection has a random component, the agent explores its action and observation spaces.

• false — Force the agent to use the base agent greedy policy (the action with maximum likelihood) when selecting actions in sim and generatePolicyFunction. Specifically, in this case the agent uses the rlMaxQPolicy policy. Since the action selection is greedy the policy behaves deterministically and the agent does not explore its action and observation spaces.

Note

This option affects only simulation and deployment; it does not affect training. When you train an agent using train, the agent always uses its exploration policy independently of the value of this property.

SampleTime — Sample time of agent

1 (default) | 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.

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.

Example: SampleTime=-1

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 DQN Agent from Observation and Action Specifications

Create an environment with a discrete action space, and obtain its observation and action specifications. For this example, load the environment used in the example Create DQN Agent Using Deep Network Designer and Train Using Image Observations. This environment has two observations: a 50-by-50 grayscale image and a scalar (the angular velocity of the pendulum). The action is a scalar with five possible elements (a torque of -2, -1, 0, 1, or 2 Nm applied to a swinging pole).

% Load predefined environment
env = rlPredefinedEnv("SimplePendulumWithImage-Discrete");

% Obtain observation and action specifications
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

The agent creation function initializes the actor and critic networks randomly. You can ensure reproducibility by fixing the seed of the random generator.

rng(0)

Create a deep Q-network agent from the environment observation and action specifications.

agent = rlDQNAgent(obsInfo,actInfo);

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

getAction(agent,{rand(obsInfo(1).Dimension),rand(obsInfo(2).Dimension)})

ans = 1x1 cell array
    {[1]}

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

Create DQN Agent Using Initialization Options

Create an environment with a discrete action space, and obtain its observation and action specifications. For this example, load the environment used in the example Create DQN Agent Using Deep Network Designer and Train Using Image Observations. This environment has two observations: a 50-by-50 grayscale image and a scalar (the angular velocity of the pendulum). The action is a scalar with five possible elements (a torque of either -2, -1, 0, 1, or 2 Nm applied to a swinging pole).

% Load predefined environment
env = rlPredefinedEnv("SimplePendulumWithImage-Discrete");

% Obtain observation and action specifications
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

Create an agent initialization option object, specifying that each hidden fully connected layer in the network must have 128 neurons (instead of the default number, 256).

initOpts = rlAgentInitializationOptions(NumHiddenUnit=128);

The agent creation function initializes the actor and critic networks randomly. Ensure reproducibility by fixing the seed of the random generator.

rng(0)

Create a policy gradient agent from the environment observation and action specifications.

agent = rlDQNAgent(obsInfo,actInfo,initOpts);

Extract the deep neural network from both the critic.

criticNet = getModel(getCritic(agent));

To verify that each hidden fully connected layer has 128 neurons, you can display the layers on the MATLAB® command window,

criticNet.Layers

or visualize the structure interactively using analyzeNetwork.

analyzeNetwork(criticNet)

Plot the critic network

plot(layerGraph(criticNet))

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

getAction(agent,{rand(obsInfo(1).Dimension),rand(obsInfo(2).Dimension)})

ans = 1x1 cell array
    {[0]}

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

Create DQN Agent Using a Multi-Output Critic

Create an environment interface and obtain its observation and action specifications. For this example load the predefined environment used for the Train DQN Agent to Balance Cart-Pole System example. This environment has a continuous four-dimensional observation space (the positions and velocities of both cart and pole) and a discrete one-dimensional action space consisting on the application of two possible forces, -10N or 10N.

Create the predefined environment.

env = rlPredefinedEnv("CartPole-Discrete");

Get the observation and action specification objects.

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

A DQN agent approximates the long-term reward, given observations and actions, using a parametrized Q-value function critic.

Since DQN agents have a discrete action space, you have the option to create a vector (that is a multi-output) Q-value function critic, which is generally more efficient than a comparable single-output critic. A vector Q-value function is a mapping from an environment observation to a vector in which each element represents the expected discounted cumulative long-term reward when an agent starts from the state corresponding to the given observation and executes the action corresponding to the element number (and follows a given policy afterwards).

To model the Q-value function within the critic, use a deep neural network. The network must have one input layer (which receives the content of the observation channel, as specified by obsInfo) and one output layer (which returns the vector of values for all the possible actions).

Define the network as an array of layer objects, and get the dimensions of the observation space (that is, prod(obsInfo.Dimension)) and the number of possible actions (that is, numel(actInfo.Elements)) directly from the environment specification objects.

dnn = [
    featureInputLayer(prod(obsInfo.Dimension))
    fullyConnectedLayer(24)
    reluLayer
    fullyConnectedLayer(24)
    reluLayer
    fullyConnectedLayer(numel(actInfo.Elements))
    ];

Convert the network to a dlnetwork object ad display the number of weights.

dnn = dlnetwork(dnn);
summary(dnn)

   Initialized: true

   Number of learnables: 770

   Inputs:
      1   'input'   4 features

Create the critic using rlVectorQValueFunction, the network dnn as well as the observation and action specifications.

critic = rlVectorQValueFunction(dnn,obsInfo,actInfo);

Check that the critic works with a random observation input.

getValue(critic,{rand(obsInfo.Dimension)})

ans = 2x1 single column vector

   -0.0361
    0.0913

Create the DQN agent using the critic.

agent = rlDQNAgent(critic)

agent = 
  rlDQNAgent with properties:

        ExperienceBuffer: [1x1 rl.replay.rlReplayMemory]
            AgentOptions: [1x1 rl.option.rlDQNAgentOptions]
    UseExplorationPolicy: 0
         ObservationInfo: [1x1 rl.util.rlNumericSpec]
              ActionInfo: [1x1 rl.util.rlFiniteSetSpec]
              SampleTime: 1

Specify agent options, including training options for the critic.

agent.AgentOptions.UseDoubleDQN=false;
agent.AgentOptions.TargetUpdateMethod="periodic";
agent.AgentOptions.TargetUpdateFrequency=4;
agent.AgentOptions.ExperienceBufferLength=100000;
agent.AgentOptions.DiscountFactor=0.99;
agent.AgentOptions.MiniBatchSize=256;

agent.AgentOptions.CriticOptimizerOptions.LearnRate=1e-2;
agent.AgentOptions.CriticOptimizerOptions.GradientThreshold=1;

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

getAction(agent,{rand(obsInfo.Dimension)})

ans = 1x1 cell array
    {[10]}

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

Create a DQN Agent Using a Single-Output Critic

Create an environment interface and obtain its observation and action specifications. For this example load the predefined environment used for the Train DQN Agent to Balance Cart-Pole System example. This environment has a continuous four-dimensional observation space (the positions and velocities of both cart and pole) and a discrete one-dimensional action space consisting on the application of two possible forces, -10 N or 10 N.

Create the predefined environment.

env = rlPredefinedEnv("CartPole-Discrete");

Get the observation and action specification objects.

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

For DQN agents, you can use multi-output Q-value function critics, which are generally more efficient than a comparable single-output critics. However, for this example, create a single-output Q-value function critic instead.

A Q-value function critic 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).

To model the parametrized Q-value function within the critic, use a neural network with two input layers (one for the observation channel, as specified by obsInfo, and the other for the action channel, as specified by actInfo) and one output layer (which returns the scalar value).

Note that prod(obsInfo.Dimension) and prod(actInfo.Dimension) return the number of dimensions of the observation and action spaces, respectively, regardless of whether they are arranged as row vectors, column vectors, or matrices.

Define each network path as an array of layer objects, and assign names to the input and output layers of each path, so you can connect the paths.

% Observation path
obsPath = [
    featureInputLayer(prod(obsInfo.Dimension),Name="netOin")
    fullyConnectedLayer(24)
    reluLayer
    fullyConnectedLayer(24,Name="fcObsPath")
    ];

% Action path
actPath = [
    featureInputLayer(prod(actInfo.Dimension),Name="netAin")
    fullyConnectedLayer(24,Name="fcActPath")
    ];

% Common  path (concatenate inputs along dim #1)
commonPath = [
    concatenationLayer(1,2,Name="cat")
    reluLayer
    fullyConnectedLayer(1,Name="out")
    ];

% Add paths to network
net = layerGraph;
net = addLayers(net,obsPath);
net = addLayers(net,actPath);
net = addLayers(net,commonPath);

% Connect layers
net = connectLayers(net,"fcObsPath","cat/in1");
net = connectLayers(net,"fcActPath","cat/in2");

% Plot network
plot(net)

% Convert to dlnetwork object
net = dlnetwork(net);

% Display the number of weights
summary(net)

   Initialized: true

   Number of learnables: 817

   Inputs:
      1   'netOin'   4 features
      2   'netAin'   1 features

Create the critic approximator object using net, the environment observation and action specifications, and the names of the network input layers to be connected with the environment observation and action channels. For more information, see rlQValueFunction.

critic = rlQValueFunction(net, ...
    obsInfo, ...
    actInfo, ...
    ObservationInputNames="netOin", ...
    ActionInputNames="netAin");

Check the critic with a random observation and action input.

getValue(critic,{rand(obsInfo.Dimension)},{rand(actInfo.Dimension)})

ans = single
    -0.0232

Create the DQN agent using the critic.

agent = rlDQNAgent(critic)

agent = 
  rlDQNAgent with properties:

        ExperienceBuffer: [1x1 rl.replay.rlReplayMemory]
            AgentOptions: [1x1 rl.option.rlDQNAgentOptions]
    UseExplorationPolicy: 0
         ObservationInfo: [1x1 rl.util.rlNumericSpec]
              ActionInfo: [1x1 rl.util.rlFiniteSetSpec]
              SampleTime: 1

Specify agent options, including training options for the critic.

agent.AgentOptions.UseDoubleDQN=false;
agent.AgentOptions.TargetUpdateMethod="periodic";
agent.AgentOptions.TargetUpdateFrequency=4;
agent.AgentOptions.ExperienceBufferLength=100000;
agent.AgentOptions.DiscountFactor=0.99;
agent.AgentOptions.MiniBatchSize=256;

agent.AgentOptions.CriticOptimizerOptions.LearnRate=1e-2;
agent.AgentOptions.CriticOptimizerOptions.GradientThreshold=1;

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

getAction(agent,{rand(obsInfo.Dimension)})

ans = 1x1 cell array
    {[10]}

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

Create DQN Agent with Recurrent Neural Network

For this example load the predefined environment used for the Train DQN Agent to Balance Cart-Pole System example. This environment has a continuous four-dimensional observation space (the positions and velocities of both cart and pole) and a discrete one-dimensional action space consisting on the application of two possible forces, -10N or 10N.

env = rlPredefinedEnv("CartPole-Discrete");

Get the observation and action specification objects.

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

For DQN agents, only the vector function approximator, rlVectorQValueFunction, supports recurrent neural networks models. The network must have one input layer (taking the content of the observation channel) and one output layer (returning the vector of values for all the possible actions).

Define the network as an array of layer objects. To create a recurrent neural network, use a sequenceInputLayer as the input layer and include at least one lstmLayer.

net = [
    sequenceInputLayer(prod(obsInfo.Dimension))
    fullyConnectedLayer(50)
    reluLayer
    lstmLayer(20,OutputMode="sequence");
    fullyConnectedLayer(20)
    reluLayer
    fullyConnectedLayer(numel(actInfo.Elements))
    ];

Convert to a dlnetwork object and display the number of weights.

net = dlnetwork(net);
summary(net);

   Initialized: true

   Number of learnables: 6.3k

   Inputs:
      1   'sequenceinput'   Sequence input with 4 dimensions

Create the critic approximator object using net and the environment specifications.

critic = rlVectorQValueFunction(net,obsInfo,actInfo);

Check your critic with a random input observation.

getValue(critic,{rand(obsInfo.Dimension)})

ans = 2x1 single column vector

    0.0136
    0.0067

Define some training options for the critic.

criticOptions = rlOptimizerOptions( ...
    LearnRate=1e-3, ...
    GradientThreshold=1);

Specify options for creating the DQN agent. To use a recurrent neural network, you must specify a SequenceLength greater than 1.

agentOptions = rlDQNAgentOptions(...
    UseDoubleDQN=false, ...
    TargetSmoothFactor=5e-3, ...
    ExperienceBufferLength=1e6, ...
    SequenceLength=32, ...
    CriticOptimizerOptions=criticOptions);

agentOptions.EpsilonGreedyExploration.EpsilonDecay = 1e-4;

Create the agent. The actor and critic networks are initialized randomly.

agent = rlDQNAgent(critic,agentOptions)

agent = 
  rlDQNAgent with properties:

        ExperienceBuffer: [1x1 rl.replay.rlReplayMemory]
            AgentOptions: [1x1 rl.option.rlDQNAgentOptions]
    UseExplorationPolicy: 0
         ObservationInfo: [1x1 rl.util.rlNumericSpec]
              ActionInfo: [1x1 rl.util.rlFiniteSetSpec]
              SampleTime: 1

Check your agent using getAction to return the action from a random observation.

getAction(agent,rand(obsInfo.Dimension))

ans = 1x1 cell array
    {[-10]}

To evaluate the agent using sequential observations, use the sequence length (time) dimension. For example, obtain actions for a sequence of 9 observations.

[action,state] = getAction(agent, ...
    {rand([obsInfo.Dimension 1 9])});

Display the action corresponding to the seventh element of the observation.

action = action{1};
action(1,1,1,7)

ans = -10

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

Version History

Introduced in R2019a