rlDDPGAgent
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
Description
Create Agent from Observation and Action Specifications
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 observation
specification agent
= rlDDPGAgent(observationInfo
,actionInfo
)observationInfo
and the action specification
actionInfo
. The ObservationInfo
and
ActionInfo
properties of agent
are set to
the observationInfo
and actionInfo
input
arguments, respectively.
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 agent
= rlDDPGAgent(observationInfo
,actionInfo
,initOpts
)initOpts
object. For more information on
the initialization options, see rlAgentInitializationOptions
.
Create Agent from Actor and Critic
Specify Agent Options
creates a DDPG agent and sets the agent
= rlDDPGAgent(___,agentOptions
)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.
actor
— Actor
rlContinuousDeterministicActor
object
Actor, specified as an rlContinuousDeterministicActor
. For more information on creating actors,
see Create Policies and Value Functions.
critic
— Critic
rlQValueFunction
object
Critic, specified as an rlQValueFunction
object. For more information on creating critics, see
Create Policies and Value Functions.
Properties
ObservationInfo
— Observation specifications
specification object | array of specification objects
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 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
.
Example: [rlNumericSpec([2 1])
rlFiniteSetSpec([3,5,7])]
ActionInfo
— Action specification
rlNumericSpec
object
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: 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
.
ExperienceBuffer
— Experience buffer
rlReplayMemory
object | rlPrioritizedReplayMemory
object | rlHindsightReplayMemory
object | rlHindsightPrioritizedReplayMemory
object
Experience buffer, specified as one of the following replay memory objects.
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
false
(default) | true
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 insim
andgeneratePolicyFunction
. Specifically, in this case the agent uses therlAdditiveNoisePolicy
. 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 insim
andgeneratePolicyFunction
. Specifically, in this case the agent uses therlDeterministicActorPolicy
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 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.
Within a Simulink® environment, the RL Agent block
that uses the agent object executes every SampleTime
seconds of
simulation time. If SampleTime
is -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.
Note
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.
Regardless of the type of environment, the time interval between consecutive elements
in the output experience returned by sim
or
train
is
always SampleTime
.
If SampleTime
is -1
, for 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, while for MATLAB environments, this time interval is considered equal to
1
.
This property is shared between the agent and the agent options object within the agent. Therefore, if you change it in the agent options object, it gets changed in the agent, and vice versa.
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 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. 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 = rlDDPGAgent(obsInfo,actInfo)
agent = rlDDPGAgent with properties: ExperienceBuffer: [1x1 rl.replay.rlReplayMemory] AgentOptions: [1x1 rl.option.rlDDPGAgentOptions] UseExplorationPolicy: 0 ObservationInfo: [1x1 rl.util.rlNumericSpec] ActionInfo: [1x1 rl.util.rlNumericSpec] SampleTime: 1
To check your agent, use getAction
to return the action from a random observation.
getAction(agent,{rand(obsInfo(1).Dimension)})
ans = 1x1 cell array
{[0.0182]}
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 to Swing Up and Balance Pendulum with 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)
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 getAction
to return the action from a random observation.
getAction(agent,{rand(obsInfo(1).Dimension),rand(obsInfo(2).Dimension)})
ans = 1x1 cell array
{[-0.0364]}
You can now test and train the agent within the environment.
Create DDPG Agent from Actor and Critic
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. Ensure reproducibility by fixing the seed of the random generator.
rng(0)
DDPG agents use a parametrized 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 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. 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 parametrized 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 parametrized 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 = 1x1 cell array
{[-0.5493]}
Create the DDPG agent using the actor and critic.
agent = rlDDPGAgent(actor,critic)
agent = rlDDPGAgent with properties: ExperienceBuffer: [1x1 rl.replay.rlReplayMemory] AgentOptions: [1x1 rl.option.rlDDPGAgentOptions] UseExplorationPolicy: 0 ObservationInfo: [1x1 rl.util.rlNumericSpec] ActionInfo: [1x1 rl.util.rlNumericSpec] SampleTime: 1
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 = 1x1 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 parametrized Q-value function approximator to estimate the value of the policy.
To model the parametrized 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: 10.8k Inputs: 1 'netOin' Sequence input with 2 dimensions 2 'netAin' Sequence input with 1 dimensions
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 random observation and action inputs.
getValue(critic,{rand(obsInfo.Dimension)},{rand(actInfo.Dimension)})
ans = single
-0.0074
DDPG agents use a continuous deterministic actor to approximate the policy. Since 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 dimensions
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 random observation input.
getAction(actor,{rand(obsInfo.Dimension)})
ans = 1x1 cell array
{[0.0246]}
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: [1x1 rl.replay.rlReplayMemory] AgentOptions: [1x1 rl.option.rlDDPGAgentOptions] UseExplorationPolicy: 0 ObservationInfo: [1x1 rl.util.rlNumericSpec] ActionInfo: [1x1 rl.util.rlNumericSpec] SampleTime: 0.1000
To check your agent return the action from a random observation.
getAction(agent,{rand(obsInfo.Dimension)})
ans = 1x1 cell array
{[0.0158]}
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 = 0.0780
You can now test and train the agent within the environment.
Version History
Introduced in R2019a
See Also
Apps
Functions
getAction
|getActor
|getCritic
|generatePolicyFunction
|generatePolicyBlock
|getActionInfo
|getObservationInfo
Objects
rlDDPGAgentOptions
|rlAgentInitializationOptions
|rlQValueFunction
|rlContinuousDeterministicActor
|rlTD3Agent
|rlSACAgent
Blocks
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