rlPPOAgent
Proximal policy optimization reinforcement learning agent
Description
Proximal policy optimization (PPO) is a model-free, online, on-policy, policy gradient reinforcement learning method. This algorithm alternates between sampling data through environmental interaction and optimizing a clipped surrogate objective function using stochastic gradient descent. The action space can be either discrete or continuous.
For more information on PPO agents, see Proximal Policy Optimization Agents. 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 proximal policy optimization (PPO) 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
= rlPPOAgent(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 PPO agent for an environment with the given observation and action
specifications. The agent uses default networks configured using options specified in
the agent
= rlPPOAgent(observationInfo
,actionInfo
,initOpts
)initOpts
object. Actor-critic agents do not support recurrent
neural networks. For more information on the initialization options, see rlAgentInitializationOptions
.
Create Agent from Actor and Critic
Specify Agent Options
creates a PPO agent and sets the AgentOptions
property to the agent
= rlPPOAgent(___,agentOptions
)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
rlDiscreteCategoricalActor
object | rlContinuousGaussianActor
object
Actor that implements the policy, specified as an rlDiscreteCategoricalActor
or rlContinuousGaussianActor
function approximator object. For more
information on creating actor approximators, see Create Policies and Value Functions.
critic
— Critic
rlValueFunction
object
Critic that estimates the discounted long-term reward, specified as an rlValueFunction
object. For more information on creating critic approximators, see Create Policies and Value Functions.
Your critic can use a recurrent neural network as its function approximator. In this case, your actor must also use a recurrent neural network. For an example, see Create PPO Agent with Recurrent Neural Networks.
Properties
ObservationInfo
— Observation specifications
specification object | array of specification objects
Observation specifications, specified as a reinforcement learning specification object or an array of specification objects defining properties such as dimensions, data type, and names of the observation signals.
If you create the agent by specifying an actor and critic, the value of
ObservationInfo
matches the value specified in the actor and
critic objects.
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
specification object
Action specifications, specified as a reinforcement learning specification object defining properties such as dimensions, data type, and names of the action signals.
For a discrete action space, you must specify actionInfo
as an
rlFiniteSetSpec
object.
For a continuous action space, you must specify actionInfo
as
an rlNumericSpec
object.
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.
You can extract actionInfo
from an existing environment or
agent using getActionInfo
.
You can also construct the specification manually using rlFiniteSetSpec
or rlNumericSpec
.
AgentOptions
— Agent options
rlPPOAgentOptions
object
Agent options, specified as an rlPPOAgentOptions
object.
UseExplorationPolicy
— Option to use exploration policy
true
(default) | false
Option to use exploration policy when selecting actions, specified as a one of the following logical values.
true
— Use the base agent exploration policy when selecting actions insim
andgeneratePolicyFunction
. In this case, the agent selects its actions by sampling its probability distribution, the policy is therefore stochastic and the agent explores its observation space.false
— Use the base agent greedy policy (the action with maximum likelihood) when selecting actions insim
andgeneratePolicyFunction
. In this case, the simulated agent and generated policy behave deterministically.
Note
This option affects only simulation and deployment; it does not affect training.
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 | Get actor from reinforcement learning agent |
setActor | Set actor of reinforcement learning agent |
getCritic | Get critic from reinforcement learning agent |
setCritic | Set critic of reinforcement learning agent |
generatePolicyFunction | Generate function that evaluates policy of an agent or policy object |
Examples
Create Discrete PPO 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 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).
env = rlPredefinedEnv("SimplePendulumWithImage-Discrete");
Obtain observation and action specifications from the environment.
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 PPO agent from the environment observation and action specifications.
agent = rlPPOAgent(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
{[-2]}
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 Continuous PPO 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");
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 PPO actor-critic agent from the environment observation and action specifications.
agent = rlPPOAgent(obsInfo,actInfo,initOpts);
Extract the deep neural networks from both the agent actor and critic.
actorNet = getModel(getActor(agent)); criticNet = getModel(getCritic(agent));
Display the layers of the critic network, and verify that each hidden fully connected layer has 128 neurons
criticNet.Layers
ans = 11x1 Layer array with layers: 1 'concat' Concatenation Concatenation of 2 inputs along dimension 1 2 'relu_body' ReLU ReLU 3 'fc_body' Fully Connected 128 fully connected layer 4 'body_output' ReLU ReLU 5 'input_1' Image Input 50x50x1 images 6 'conv_1' 2-D Convolution 64 3x3x1 convolutions with stride [1 1] and padding [0 0 0 0] 7 'relu_input_1' ReLU ReLU 8 'fc_1' Fully Connected 128 fully connected layer 9 'input_2' Feature Input 1 features 10 'fc_2' Fully Connected 128 fully connected layer 11 'output' Fully Connected 1 fully connected layer
Plot actor and critic networks
plot(layerGraph(actorNet))
plot(layerGraph(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
{[0.9228]}
You can now test and train the agent within the environment.
Create Proximal Policy Optimization Agent
Create an environment interface, and obtain its observation and action specifications.
env = rlPredefinedEnv("CartPole-Discrete");
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);
For PPO agents, the critic estimates a value function, therefore it must take the observation signal as input and return a scalar value. Create a deep neural network to be used as approximation model within the critic. Define the network as an array of layer objects.
criticNet = [ featureInputLayer(prod(obsInfo.Dimension)) fullyConnectedLayer(100) reluLayer fullyConnectedLayer(1) ];
Convert to a dlnetwork
object and display the number of parameters.
criticNet = dlnetwork(criticNet); summary(criticNet)
Initialized: true Number of learnables: 601 Inputs: 1 'input' 4 features
Create the critic using criticNet
. PPO agents use an rlValueFunction
object to implement the critic.
critic = rlValueFunction(criticNet,obsInfo);
Check the critic with a random observation input.
getValue(critic,{rand(obsInfo.Dimension)})
ans = single
-0.2479
To approximate the policy within the actor use a neural network. For PPO agents, the actor executes a stochastic policy, which for discrete action spaces is implemented by a discrete categorical actor. In this case the approximator must take the observation signal as input and return a probability for each action. Therefore the output layer must have as many elements as the number of possible actions.
Define the network as an array of layer objects, getting the dimension of the observation space and the number of possible actions from the environment specification objects.
actorNet = [ featureInputLayer(prod(obsInfo.Dimension)) fullyConnectedLayer(200) reluLayer fullyConnectedLayer(numel(actInfo.Dimension)) ];
Convert to a dlnetwork
object and display the number of parameters.
actorNet = dlnetwork(actorNet); summary(actorNet)
Initialized: true Number of learnables: 1.4k Inputs: 1 'input' 4 features
Create the actor using actorNet
. PPO agents use an rlDiscreteCategoricalActor
object to implement the actor for discrete action spaces.
actor = rlDiscreteCategoricalActor(actorNet,obsInfo,actInfo);
Check the actor with a random observation input.
getAction(actor,{rand(obsInfo.Dimension)})
ans = 1x1 cell array
{[-10]}
Create a PPO agent using the actor and the critic.
agent = rlPPOAgent(actor,critic)
agent = rlPPOAgent with properties: AgentOptions: [1x1 rl.option.rlPPOAgentOptions] UseExplorationPolicy: 1 ObservationInfo: [1x1 rl.util.rlNumericSpec] ActionInfo: [1x1 rl.util.rlFiniteSetSpec] SampleTime: 1
Specify agent options, including training options for the actor and the critic.
agent.AgentOptions.ExperienceHorizon = 1024; agent.AgentOptions.DiscountFactor = 0.95; agent.AgentOptions.CriticOptimizerOptions.LearnRate = 8e-3; agent.AgentOptions.CriticOptimizerOptions.GradientThreshold = 1; agent.AgentOptions.ActorOptimizerOptions.LearnRate = 8e-3; agent.AgentOptions.ActorOptimizerOptions.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 against the environment.
Create Continuous PPO Agent
Create an environment with a continuous action space, and obtain its observation and action specifications. For this example, load the double integrator continuous action space environment used in the example Train DDPG Agent to Control Double Integrator System. 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");
obsInfo = getObservationInfo(env)
obsInfo = rlNumericSpec with properties: LowerLimit: -Inf UpperLimit: Inf Name: "states" Description: "x, dx" Dimension: [2 1] DataType: "double"
actInfo = getActionInfo(env)
actInfo = rlNumericSpec with properties: LowerLimit: -Inf UpperLimit: Inf Name: "force" Description: [0x0 string] Dimension: [1 1] DataType: "double"
In this example, the action is a scalar value representing a force ranging from -2 to 2 Newton. To make sure that the output from the agent is in this range, you perform an appropriate scaling operation. Store these limits so you can easily access them later.
actInfo.LowerLimit=-2; actInfo.UpperLimit=2;
The actor and critic networks are initialized randomly. Ensure reproducibility by fixing the seed of the random generator.
rng(0)
For PPO agents, the critic estimates a value function, therefore it must take the observation signal as input and return a scalar value. To approximate the value function within the critic, use a neural network. Define the network as an array of layer objects.
criticNet = [ featureInputLayer(prod(obsInfo.Dimension)) fullyConnectedLayer(100) reluLayer fullyConnectedLayer(1)];
Convert to a dlnetwork
object and display the number of parameters.
criticNet = dlnetwork(criticNet); summary(criticNet)
Initialized: true Number of learnables: 401 Inputs: 1 'input' 2 features
Create the critic using criticNet
. PPO agents use an rlValueFunction
object to implement the critic.
critic = rlValueFunction(criticNet,obsInfo);
Check the critic with a random observation input.
getValue(critic,{rand(obsInfo.Dimension)})
ans = single
-0.0899
To approximate the policy within the actor, use a neural network. For PPO agents, the actor executes a stochastic policy, which for continuous action spaces is implemented by a continuous Gaussian actor. In this case the network must take the observation signal as input and return both a mean value and a standard deviation value for each action. Therefore it must have two output layers (one for the mean values the other for the standard deviation values), each having as many elements as the dimension of the action space.
Note that standard deviations must be nonnegative and mean values must fall within the range of the action. Therefore the output layer that returns the standard deviations must be a softplus or ReLU layer, to enforce nonnegativity, while the output layer that returns the mean values must be a scaling layer, to scale the mean values to the output range.
Define each network path as an array of layer objects. Get the dimensions of the observation and action spaces, and the action range limits from the environment specification objects. Specify a name for the input and output layers, so you can later explicitly associate them with the appropriate environment channel.
% Define common input path layer commonPath = [ featureInputLayer(prod(obsInfo.Dimension),Name="comPathIn") fullyConnectedLayer(100) reluLayer fullyConnectedLayer(1,Name="comPathOut") ]; % Define mean value path meanPath = [ fullyConnectedLayer(15,Name="meanPathIn") reluLayer fullyConnectedLayer(prod(actInfo.Dimension)); tanhLayer; scalingLayer(Name="meanPathOut",Scale=actInfo.UpperLimit) ]; % Define standard deviation path sdevPath = [ fullyConnectedLayer(15,'Name',"stdPathIn") reluLayer fullyConnectedLayer(prod(actInfo.Dimension)); softplusLayer(Name="stdPathOut") ]; % Add layers to layerGraph object actorNet = layerGraph(commonPath); actorNet = addLayers(actorNet,meanPath); actorNet = addLayers(actorNet,sdevPath); % Connect paths actorNet = connectLayers(actorNet,"comPathOut","meanPathIn/in"); actorNet = connectLayers(actorNet,"comPathOut",'stdPathIn/in'); % Plot network plot(actorNet)
% Convert to dlnetwork and display number of weights
actorNet = dlnetwork(actorNet);
summary(actorNet)
Initialized: true Number of learnables: 493 Inputs: 1 'comPathIn' 2 features
Create the actor using actorNet
. PPO agents use an rlContinuousGaussianActor
object to implement the actor for continuous action spaces.
actor = rlContinuousGaussianActor(actorNet, obsInfo, actInfo, ... 'ActionMeanOutputNames',"meanPathOut",... 'ActionStandardDeviationOutputNames',"stdPathOut",... 'ObservationInputNames',"comPathIn");
Check the actor with a random observation input.
getAction(actor,{rand(obsInfo.Dimension)})
ans = 1x1 cell array
{[-0.2267]}
Create a PPO agent using the actor and the critic.
agent = rlPPOAgent(actor,critic)
agent = rlPPOAgent with properties: AgentOptions: [1x1 rl.option.rlPPOAgentOptions] UseExplorationPolicy: 1 ObservationInfo: [1x1 rl.util.rlNumericSpec] ActionInfo: [1x1 rl.util.rlNumericSpec] SampleTime: 1
Specify agent options, including training options for the actor and the critic.
agent.AgentOptions.ExperienceHorizon = 1024; agent.AgentOptions.DiscountFactor = 0.95; agent.AgentOptions.CriticOptimizerOptions.LearnRate = 8e-3; agent.AgentOptions.CriticOptimizerOptions.GradientThreshold = 1; agent.AgentOptions.ActorOptimizerOptions.LearnRate = 8e-3; agent.AgentOptions.ActorOptimizerOptions.GradientThreshold = 1;
Specify training options for the critic.
criticOpts = rlOptimizerOptions( ... 'LearnRate',8e-3,'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
{[0.2719]}
You can now test and train the agent within the environment.
Create PPO Agent with Recurrent Neural Networks
For this example load the predefined environment used for the Train DQN Agent to Balance Cart-Pole System example.
env = rlPredefinedEnv("CartPole-Discrete");
Get observation and action information. 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.
obsInfo = getObservationInfo(env); actInfo = getActionInfo(env);
For PPO agents, the critic estimates a value function, therefore it must take the observation signal as input and return a scalar value. To approximate the value function within the critic, use a neural network.
Define the network as an array of layer objects, and get the dimensions of the observation space from the environment specification object. To create a recurrent neural network, use a sequenceInputLayer
as the input layer and include an lstmLayer
as one of the other network layers.
criticNet = [ sequenceInputLayer(prod(obsInfo.Dimension)) fullyConnectedLayer(8) reluLayer lstmLayer(8) fullyConnectedLayer(1)];
Convert to a dlnetwork
object and display the number of learnable parameters.
criticNet = dlnetwork(criticNet); summary(criticNet)
Initialized: true Number of learnables: 593 Inputs: 1 'sequenceinput' Sequence input with 4 dimensions
Create the critic using criticNetwork
. PPO agents use an rlValueFunction
object to implement the critic.
critic = rlValueFunction(criticNet,obsInfo);
Check the critic with a random observation input.
getValue(critic,{rand(obsInfo.Dimension)})
ans = single
0.0017
Since the critic has a recurrent network, the actor must have a recurrent network too. For PPO agents, the actor executes a stochastic policy, which for discrete action spaces is implemented by a discrete categorical actor. In this case the network must take the observation signal as input and return a probability for each action. Therefore the output layer must have as many elements as the number of possible actions.
Define the network as an array of layer objects, and get the dimension of the observation space and the number of possible actions from the environment specification objects.
actorNet = [ sequenceInputLayer(prod(obsInfo.Dimension)) fullyConnectedLayer(100) reluLayer lstmLayer(8) fullyConnectedLayer(numel(actInfo.Elements)) softmaxLayer ];
Convert the network to a dlnetwork
object and display the number of learnable parameters.
actorNet = dlnetwork(actorNet); summary(actorNet)
Initialized: true Number of learnables: 4k Inputs: 1 'sequenceinput' Sequence input with 4 dimensions
Create the actor using actorNetwork
. PPO agents use an rlDiscreteCategoricalActor
object to implement the actor for discrete action spaces.
actor = rlDiscreteCategoricalActor(actorNet,obsInfo,actInfo);
Check the actor with a random observation input.
getAction(actor,{rand(obsInfo.Dimension)})
ans = 1x1 cell array
{[-10]}
Set some training option for the critic.
criticOptions = rlOptimizerOptions( ... LearnRate=1e-2, ... GradientThreshold=1);
Set some training options for the actor.
actorOptions = rlOptimizerOptions( ... LearnRate=1e-3, ... GradientThreshold=1);
Create the agent options object.
agentOptions = rlPPOAgentOptions(... AdvantageEstimateMethod="finite-horizon", ... ClipFactor=0.1, ... CriticOptimizerOptions=criticOptions, ... ActorOptimizerOptions=actorOptions);
When recurrent neural networks are used, the MiniBatchSize
property is the length of the learning trajectory.
agentOptions.MiniBatchSize
ans = 128
Create the agent using the actor and critic, as well as the agent options object.
agent = rlPPOAgent(actor,critic,agentOptions);
Check your agent with a random observation input.
getAction(agent,rand(obsInfo.Dimension))
ans = 1x1 cell array
{[-10]}
Tips
For continuous action spaces, this agent does not enforce the constraints set by the action specification. In this case, you must enforce action space constraints within the environment.
While tuning the learning rate of the actor network is necessary for PPO agents, it is not necessary for TRPO agents.
Version History
Introduced in R2019b
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