rlDDPGAgent

R2026b

Deep deterministic policy gradient (DDPG) reinforcement learning agent

Description

The deep deterministic policy gradient (DDPG) algorithm is an off-policy actor-critic method for environments with a continuous action-space. A DDPG agent learns a deterministic policy while also using a Q-value function critic to estimate the value of the optimal policy. It features a target actor and critic as well as an experience buffer. DDPG agents supports offline training (training from saved data, without an environment).

For more information, see Deep Deterministic Policy Gradient (DDPG) Agent. For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.

Creation

Syntax

agent = rlDDPGAgent(observationInfo,actionInfo)

agent = rlDDPGAgent(observationInfo,actionInfo,initOpts)

agent = rlDDPGAgent(actor,critic,agentOptions)

agent = rlDDPGAgent(___,agentOptions)

Description

Create Default Agent from Observation and Action Specifications

agent = rlDDPGAgent(observationInfo,actionInfo) creates a deep deterministic policy gradient agent for an environment with the given observation and action specifications, using default initialization options. The actor and critic in the agent use default deep neural networks built from the specifications.

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

Create Agent from Actor and Critic

agent = rlDDPGAgent(actor,critic,agentOptions) creates a DDPG agent with the specified actor and critic, using default DDPG agent options.

Specify Agent Options

agent = rlDDPGAgent(___,agentOptions) creates a DDPG agent 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

observationInfo — Observation specifications

rlFiniteSetSpec object | rlNumericSpec object | array

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

This argument sets the ObservationInfo property.

Example: observationInfo=[rlNumericSpec([2 1]) rlFiniteSetSpec([-1 1])]

actionInfo — Action specification

rlNumericSpec object

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

This argument sets the ActionInfo property.

Example: actionInfo=rlNumericSpec([2 1])

initOpts — Agent initialization options

rlAgentInitializationOptions object

Agent initialization options, specified as an rlAgentInitializationOptions object.

Example: rlAgentInitializationOptions(NumHiddenUnit=128)

actor — Actor

rlContinuousDeterministicActor object

Actor, specified as an rlContinuousDeterministicActor. For more information on creating actors, see Create Actors, Critics, and Policy Objects.

Example: rlContinuousDeterministicActor(dlnetwork([featureInputLayer(2) fullyConnectedLayer(10) reluLayer fullyConnectedLayer(1)]),rlNumericSpec([2 1]),rlNumericSpec([1 1]))

critic — Critic

rlQValueFunction object

Critic, specified as an rlQValueFunction object. For more information on creating critics, see Create Actors, Critics, and Policy Objects.

agentOptions — Agent options

rlDDPGAgentOptions object

Agent options, specified as an rlDDPGAgentOptions object.

This argument sets the AgentOptions property.

Example: rlDDPGAgentOptions(ExperienceBufferLength=20000)

Properties

ObservationInfo — Observation specifications

Read-only: specification object | array of specification objects

This property is read-only.

Observation specifications, returned as an rlFiniteSetSpec or rlNumericSpec object or an array containing any combination 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 an actor or critic, the value of ObservationInfo matches the value specified in the actor and critic objects. If you create a default agent, the agent constructor function sets the ObservationInfo property to the input argument observationInfo.

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

ActionInfo — Action specification

Read-only: rlNumericSpec object

This property is read-only.

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

Note

For this agent, only one action channel is allowed.

If you create the agent by specifying an actor and critic, the value of ActionInfo matches the value specified in the actor and critic objects. If you create a default agent, the agent constructor function sets the ActionInfo property to the input argument ActionInfo.

You can extract actionInfo from an existing environment, function approximator, or agent using getActionInfo. You can also construct the specification manually using rlNumericSpec.

Example: ActionInfo=rlNumericSpec([2 1])

AgentOptions — Agent options

rlDDPGAgentOptions object

Agent options, specified as an rlDDPGAgentOptions object.

If you create a DDPG agent with default actor and critic that use recurrent neural networks, the default value of AgentOptions.SequenceLength is 32.

Example: myagent.AgentOptions = rlDDPGAgentOptions(ExperienceBufferLength=20000)

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.

Example: myagent.ExperienceBuffer = rlReplayMemory(rlNumericSpec([3 1]),rlFiniteSetSpec([-1 0 1]))

UseExplorationPolicy — Option to use exploration policy for simulation and deployment

false (default) | true

Option to use an exploration policy when selecting actions during simulation or after deployment, specified as a logical value.

• true — Specify this value to use the base agent exploration policy when you use the agent with the sim and generatePolicyFunction functions. Specifically, in this case the agent uses the rlAdditiveNoisePolicy object. The action selection has a random component, so the agent explores its action and observation spaces.

• false — Specify this value to force the agent to use the base agent greedy policy (the action with maximum likelihood) when you use the agent with the sim and generatePolicyFunction functions. Specifically, in this case the agent uses the rlDeterministicActorPolicy policy. The action selection is greedy, so the policy behaves deterministically and the agent does not explore its action and observation spaces.

Note

This option affects only simulation and deployment and does not affect training. When you train an agent using the train function, the agent always uses its exploration policy independently of the value of this property. Specifically, the training algorithm temporarily sets UseExplorationPolicy to true for the duration of the training,and then reverts it to the original value when the training is completed.

Example: myagent.UseExplorationPolicy = true

SampleTime — Sample time of agent

1 (default) | positive scalar | -1

Sample time of the agent, specified as a positive scalar or as -1.

Within a MATLAB^® environment, the agent is executed every time the environment advances, so, SampleTime does not affect the timing of the agent execution. If SampleTime is set to -1, in MATLAB environments, the time interval between consecutive elements in the returned output experience is considered equal to 1.

Within a Simulink^® environment, the RL Agent block that uses the agent object executes every SampleTime seconds of simulation time. If SampleTime is set to -1 the block inherits the sample time from its input signals. Set SampleTime to -1 when the block is a child of an event-driven subsystem.

Set SampleTime to a positive scalar when the block is not a child of an event-driven subsystem. Doing so ensures that the block executes at appropriate intervals when input signal sample times change due to model variations. If SampleTime is a positive scalar, this value is also the time interval between consecutive elements in the output experience returned by sim or train, regardless of the type of environment.

If SampleTime is set to -1, in Simulink environments, the time interval between consecutive elements in the returned output experience reflects the timing of the events that trigger the RL Agent block execution.

This property is shared between the agent and the agent options object within the agent. If you change this property in the agent options object, it also changes in the agent, and vice versa.

Example: myagent.SampleTime = -1 sets the sample time of the agent object myagent to -1.

UseGPUForLearning — Option to use GPU for learning

"off" (default) | "on" | "auto"

Option to use GPU for learning, specified as "off", "on", or "auto".

Setting this option to "on" configures agent learnables, targets, and optimizers for GPU usage during training. Specifically, this option lazily sets the agent approximators UseDevice property. This setting will result in an error if no GPU is available.

Setting this option to "auto" configures the agent learnables, targets, and optimizers to use a GPU during training if one is available.

Setting this option to "off" configures the agent learnables, targets, and optimizers to use the CPU during training.

The "gpu" option requires both Parallel Computing Toolbox™ software and a CUDA^® enabled NVIDIA^® GPU. For more information on supported GPUs see GPU Computing Requirements (Parallel Computing Toolbox).

You can use gpuDevice (Parallel Computing Toolbox) to query or select a local GPU device to be used with MATLAB.

Note

Training or simulating an agent on a GPU involves device-specific numerical round-off errors. Because of these errors, you can get different results on a GPU and on a CPU for the same operation.

To speed up training by using parallel processing over multiple cores, you do not need to use this property. Instead, set the UseParallel training option to "on" or "auto". For more information about training using multicore processors and GPUs for training, see Train Agents Using Parallel Computing and GPUs.

Example: myagent.UseGPUForLearning = "off"

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 Default DDPG Agent

Create an environment with a continuous action space, and obtain its observation and action specifications. For this example, load the environment used in the example Compare DDPG Agent to LQR Controller. The observation from the environment is a vector containing the position and velocity of a mass. The action is a scalar representing a force, applied to the mass, ranging continuously from -2 to 2 Newton.

env = rlPredefinedEnv("DoubleIntegrator-Continuous");

Obtain observation and action specifications.

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

The agent creation function initializes the actor and critic networks randomly. To reproduce the results of this section, specify the seed and algorithm used for random number generation.

rng(0,"twister")

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

agent = rlDDPGAgent(obsInfo,actInfo)

agent = 
  rlDDPGAgent with properties:

        ExperienceBuffer: [1×1 rl.replay.rlReplayMemory]
            AgentOptions: [1×1 rl.option.rlDDPGAgentOptions]
    UseExplorationPolicy: 0
         ObservationInfo: [1×1 rl.util.rlNumericSpec]
              ActionInfo: [1×1 rl.util.rlNumericSpec]
              SampleTime: 1
       UseGPUForLearning: "off"
         IntrinsicReward: []

To check your agent, use the getAction function to return the action from a batch of 10 random observations.

robs = rand([obsInfo(1).Dimension 10]);
a = getAction(agent,{robs});

Display the seventh element of the action batch.

a{1}(7)

ans = 
-0.0053

You can now test and train the agent within the environment. You can also use getActor and getCritic to extract the actor and critic, respectively, and getModel to extract the approximator model (by default a deep neural network) from the actor or critic.

Create Default DDPG Agent Using Initialization Options

Create an environment with a continuous action space and obtain its observation and action specifications. For this example, load the environment used in the example Train DDPG Agent with Custom Networks Using Image Observation. 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 representing a torque ranging continuously from -2 to 2 Nm.

env = rlPredefinedEnv("SimplePendulumWithImage-Continuous");

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. You can ensure reproducibility by fixing the seed of the random generator.

rng(0,"twister")

Create a DDPG agent from the environment observation and action specifications.

agent = rlDDPGAgent(obsInfo,actInfo,initOpts);

Extract the deep neural networks from both the agent actor and critic.

actorNet = getModel(getActor(agent));
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 actor and critic networks.

plot(actorNet)

plot(criticNet)

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

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

ans = 1×1 cell array
    {[-0.0364]}

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

Create DDPG Agent Using Custom Networks

Create an environment with a continuous action space and obtain its observation and action specifications. For this example, load the environment used in the example Compare DDPG Agent to LQR Controller. The observation from the environment is a vector containing the position and velocity of a mass. The action is a scalar representing a force ranging continuously from -2 to 2 Newton.

env = rlPredefinedEnv("DoubleIntegrator-Continuous");

Obtain the environment observation and action specification objects.

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

The actor and critic networks are initialized randomly. To reproduce the results of this section, specify the seed and algorithm used for random number generation.

rng(0,"twister")

DDPG agents use a parameterized Q-value function approximator to estimate the value of the policy. 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 parameterized 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. These names allow you to connect the paths and then later explicitly associate the network input and output layers with the appropriate environment channel.

% Define observation and action paths
obsPath = featureInputLayer(prod(obsInfo.Dimension),Name="obsInLyr");
actPath = featureInputLayer(prod(actInfo.Dimension),Name="actInLyr");

% Define common path: concatenate along first dimension.
commonPath = [
    concatenationLayer(1,2,Name="concat")
    fullyConnectedLayer(50)
    reluLayer
    fullyConnectedLayer(1)
    ];

Assemble dlnetwork object and add layers.

criticNet = dlnetwork;
criticNet = addLayers(criticNet, obsPath);
criticNet = addLayers(criticNet, actPath);
criticNet = addLayers(criticNet, commonPath);

Connect paths.

criticNet = connectLayers(criticNet,"obsInLyr","concat/in1");
criticNet = connectLayers(criticNet,"actInLyr","concat/in2");

Plot the network.

plot(criticNet)

Initialize the network.

criticNet = initialize(criticNet);

Display the number of weights.

summary(criticNet)

   Initialized: true

   Number of learnables: 251

   Inputs:
      1   'obsInLyr'   2 features
      2   'actInLyr'   1 features

Create the critic approximator object using criticNet, 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(criticNet,obsInfo,actInfo, ...
    ObservationInputNames="obsInLyr", ...
    ActionInputNames="actInLyr");

Check the critic with random observation and action inputs.

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

ans = single

-0.4260

DDPG agents use a parameterized deterministic policy over continuous action spaces, which is learned by a continuous deterministic actor. This actor takes the current observation as input and returns as output an action that is a deterministic function of the observation.

To model the parameterized policy within the actor, use a neural network with one input layer (which receives the content of the environment observation channel, as specified by obsInfo) and one output layer (which returns the action to the environment action channel, as specified by actInfo).

Define the network as an array of layer objects.

actorNet = [
    featureInputLayer(prod(obsInfo.Dimension))
    fullyConnectedLayer(16)
    tanhLayer
    fullyConnectedLayer(16)
    tanhLayer
    fullyConnectedLayer(prod(actInfo.Dimension))
    ];

Convert to dlnetwork object.

actorNet = dlnetwork(actorNet);

Initialize network.

actorNet = initialize(actorNet);

Display the number of weights.

summary(actorNet)

   Initialized: true

   Number of learnables: 337

   Inputs:
      1   'input'   2 features

Create the actor using actorNet and the observation and action specifications. For more information on continuous deterministic actors, see rlContinuousDeterministicActor.

actor = rlContinuousDeterministicActor(actorNet,obsInfo,actInfo);

Check the actor with a random observation input.

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

ans = 1×1 cell array
    {[-0.5493]}

Create the DDPG agent using the actor and critic.

agent = rlDDPGAgent(actor,critic)

agent = 
  rlDDPGAgent with properties:

        ExperienceBuffer: [1×1 rl.replay.rlReplayMemory]
            AgentOptions: [1×1 rl.option.rlDDPGAgentOptions]
    UseExplorationPolicy: 0
         ObservationInfo: [1×1 rl.util.rlNumericSpec]
              ActionInfo: [1×1 rl.util.rlNumericSpec]
              SampleTime: 1
       UseGPUForLearning: "off"
         IntrinsicReward: []

Specify agent options, including training options for the actor and critic.

agent.AgentOptions.SampleTime=env.Ts;
agent.AgentOptions.TargetSmoothFactor=1e-3;
agent.AgentOptions.ExperienceBufferLength=1e6;
agent.AgentOptions.DiscountFactor=0.99;
agent.AgentOptions.MiniBatchSize=32;

agent.AgentOptions.CriticOptimizerOptions.LearnRate=5e-3;
agent.AgentOptions.CriticOptimizerOptions.GradientThreshold=1;

agent.AgentOptions.ActorOptimizerOptions.LearnRate=1e-4;
agent.AgentOptions.ActorOptimizerOptions.GradientThreshold=1;

Check the agent with a random observation input.

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

ans = 1×1 cell array
    {[-0.5947]}

You can now train the agent within the environment.

Create DDPG Agent Using Custom Recurrent Neural Networks

For this example, load the environment used in the example Compare DDPG Agent to LQR Controller. The observation from the environment is a vector containing the position and velocity of a mass. The action is a scalar representing a force ranging continuously from -2 to 2 Newton.

env = rlPredefinedEnv("DoubleIntegrator-Continuous");

Get the observation and action specification objects.

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

DDPG agents use a parameterized Q-value function approximator to estimate the value of the policy.

To model the parameterized Q-value function within the critic, use a recurrent neural network, which must have two input layers and one output layer (returning the scalar value).

Define each network path as an array of layer objects. To create a recurrent neural network, use sequenceInputLayer as the input layer and include an lstmLayer as one of the other network layers.

% Define observation and action paths
obsPath = sequenceInputLayer(prod(obsInfo.Dimension),Name="netOin");
actPath = sequenceInputLayer(prod(actInfo.Dimension),Name="netAin");

% Define common path: concatenate along first dimension
commonPath = [
    concatenationLayer(1,2,Name="cat")
    lstmLayer(50)
    reluLayer
    fullyConnectedLayer(1)
    ];

Create dlnetwork object and add layers.

criticNet = dlnetwork();
criticNet = addLayers(criticNet, obsPath);
criticNet = addLayers(criticNet, actPath);
criticNet = addLayers(criticNet, commonPath);

Connect layers.

criticNet = connectLayers(criticNet,"netOin","cat/in1");
criticNet = connectLayers(criticNet,"netAin","cat/in2");

Plot network.

plot(criticNet)

Initialize network and display the number of weights.

criticNet = initialize(criticNet);
summary(criticNet)

   Initialized: true

   Number of learnables: 10.9k

   Inputs:
      1   'netOin'   Sequence input with 2 channels
      2   'netAin'   Sequence input with 1 channels

Create the critic approximator object using criticNet, 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(criticNet,obsInfo,actInfo, ...
    ObservationInputNames="netOin",ActionInputNames="netAin");

Check the critic with a batch of 10 random observations and actions.

robs = rand([obsInfo.Dimension 10]);
ract = rand([actInfo.Dimension 10]);
v = getValue(critic,{robs},{ract});

Display the sixth element of the batch.

v(6)

ans = single

-0.0078

DDPG agents use a continuous deterministic actor to approximate the policy. Because the critic has a recurrent network, you must use a recurrent network for the actor too.

Define the network as an array of layer objects.

actorNet = [
    sequenceInputLayer(prod(obsInfo.Dimension))
    lstmLayer(10)
    reluLayer
    fullyConnectedLayer(prod(actInfo.Dimension)) 
    ];

Convert to dlnetwork object, initialize network and display the number of weights.

actorNet = dlnetwork(actorNet);
actorNet = initialize(actorNet);
summary(actorNet)

   Initialized: true

   Number of learnables: 531

   Inputs:
      1   'sequenceinput'   Sequence input with 2 channels

Create the actor using actorNet and the observation and action specifications. For more information on continuous deterministic actors, see rlContinuousDeterministicActor.

actor = rlContinuousDeterministicActor(actorNet,obsInfo,actInfo);

Check the actor with a batch of 10 random observation inputs.

robs = rand([obsInfo.Dimension 10]);
a = getAction(actor,{robs});
a{1}(7)

ans = single

-0.1019

Specify some training options for the critic.

criticOpts = rlOptimizerOptions( ...
    LearnRate=5e-3,GradientThreshold=1);

Specify some training options for the actor.

actorOpts = rlOptimizerOptions( ...
    LearnRate=1e-4,GradientThreshold=1);

Specify agent options. To use a DDPG agent with recurrent neural networks, you must specify a SequenceLength greater than 1.

agentOpts = rlDDPGAgentOptions( ...
    SampleTime=env.Ts, ...
    TargetSmoothFactor=1e-3, ...
    ExperienceBufferLength=1e6, ...
    DiscountFactor=0.99, ...
    SequenceLength=20, ...
    MiniBatchSize=32, ...
    CriticOptimizerOptions=criticOpts, ...
    ActorOptimizerOptions=actorOpts);

Create the DDPG agent using the actor and critic.

agent = rlDDPGAgent(actor,critic,agentOpts)

agent = 
  rlDDPGAgent with properties:

        ExperienceBuffer: [1×1 rl.replay.rlReplayMemory]
            AgentOptions: [1×1 rl.option.rlDDPGAgentOptions]
    UseExplorationPolicy: 0
         ObservationInfo: [1×1 rl.util.rlNumericSpec]
              ActionInfo: [1×1 rl.util.rlNumericSpec]
              SampleTime: 0.1000
       UseGPUForLearning: "off"
         IntrinsicReward: []

To check your agent return the actions from a random observation.

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

ans = 1×1 cell array
    {[-0.0848]}

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

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

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

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

ans = 
-0.2942

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

Extended Capabilities

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Version History

Introduced in R2019a