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rlPPOAgent

Proximal policy optimization reinforcement learning agent

Description

The 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

Description

Create Agent from Observation and Action Specifications

example

agent = rlPPOAgent(observationInfo,actionInfo) 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 representations in the agent use default deep neural networks built from the observation specification observationInfo and the action specification actionInfo.

example

agent = rlPPOAgent(observationInfo,actionInfo,initOpts) 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 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 Representations

agent = rlPPOAgent(actor,critic) creates a PPO agent with the specified actor and critic, using the default options for the agent.

Specify Agent Options

example

agent = rlPPOAgent(___,agentOptions) creates a PPO 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

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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.

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

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.

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

Agent initialization options, specified as an rlAgentInitializationOptions object.

Actor network representation for the policy, specified as an rlStochasticActorRepresentation object. For more information on creating actor representations, see Create Policy and Value Function Representations.

Your actor representation can use a recurrent neural network as its function approximator. In this case, your critic must also use a recurrent neural network. For an example, see Create PPO Agent with Recurrent Neural Networks.

Critic network representation for estimating the discounted long-term reward, specified as an rlValueRepresentation. For more information on creating critic representations, see Create Policy and Value Function Representations.

Your critic representation 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

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Agent options, specified as an rlPPOAgentOptions object.

Object Functions

trainTrain reinforcement learning agents within a specified environment
simSimulate trained reinforcement learning agents within specified environment
getActionObtain action from agent or actor representation given environment observations
getActorGet actor representation from reinforcement learning agent
setActorSet actor representation of reinforcement learning agent
getCriticGet critic representation from reinforcement learning agent
setCriticSet critic representation of reinforcement learning agent
generatePolicyFunctionCreate function that evaluates trained policy of reinforcement learning agent

Examples

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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).

% 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. To do so, uncomment the following line.

% rng(0)

Create an actor-critic 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.

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.

% load predefined environment
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. To do so, uncomment the following line.

% rng(0)

Create a PPO actor-critic agent from the environment observation and action specifications.

agent = rlPPOAgent(obsInfo,actInfo,initOpts);

Reduce the critic learning rate to 1e-3.

critic = getCritic(agent);
critic.Options.LearnRate = 1e-3;
agent  = setCritic(agent,critic);

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 = 
  12x1 Layer array with layers:

     1   'concat'               Concatenation       Concatenation of 2 inputs along dimension 3
     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'               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'              Image Input         1x1x1 images
    10   'fc_2'                 Fully Connected     128 fully connected layer
    11   'output'               Fully Connected     1 fully connected layer
    12   'RepresentationLoss'   Regression Output   mean-squared-error

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
    {[0.9228]}

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

Create an environment interface, and obtain its observation and action specifications.

env = rlPredefinedEnv("CartPole-Discrete");
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

Create a critic representation.

% Create the network to be used as approximator in the critic.
criticNetwork = [
    featureInputLayer(4,'Normalization','none','Name','state')
    fullyConnectedLayer(1,'Name','CriticFC')];

% Set options for the critic.
criticOpts = rlRepresentationOptions('LearnRate',8e-3,'GradientThreshold',1);

% Create the critic.
critic = rlValueRepresentation(criticNetwork,obsInfo,'Observation',{'state'},criticOpts);

Create an actor representation.

% Create the network to be used as approximator in the actor.
actorNetwork = [
    featureInputLayer(4,'Normalization','none','Name','state')
    fullyConnectedLayer(2,'Name','action')];

% Set options for the actor.
actorOpts = rlRepresentationOptions('LearnRate',8e-3,'GradientThreshold',1);

% Create the actor.
actor = rlStochasticActorRepresentation(actorNetwork,obsInfo,actInfo,...
    'Observation',{'state'},actorOpts);

Specify agent options, and create a PPO agent using the environment, actor, and critic.

agentOpts = rlPPOAgentOptions(...
    'ExperienceHorizon',1024, ...
    'DiscountFactor',0.95);
agent = rlPPOAgent(actor,critic,agentOpts)
agent = 
  rlPPOAgent with properties:

    AgentOptions: [1x1 rl.option.rlPPOAgentOptions]

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

getAction(agent,{rand(4,1)})
ans = 1x1 cell array
    {[-10]}

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

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"

Since the action must be contained in a limited range, it is a good idea to set the upper and lower limit of the action signal accordingly. This must be done when the network representation for the actor has an nonlinear output layer than needs to be scaled accordingly to produce an output in the desired range.

% make sure action space upper and lower limits are finite
actInfo.LowerLimit=-2;
actInfo.UpperLimit=2;

Create a critic representation. PPO agents use a rlValueRepresentation for the critic. For continuous observation spaces, you can use either a deep neural network or a custom basis representation. For this example, create a deep neural network as the underlying approximator.

% create the network to be used as approximator in the critic
% it must take the observation signal as input and produce a scalar value
criticNet = [
    imageInputLayer([obsInfo.Dimension 1],'Normalization','none','Name','state')
    fullyConnectedLayer(10,'Name', 'fc_in')
    reluLayer('Name', 'relu')
    fullyConnectedLayer(1,'Name','out')];

% set some training options for the critic
criticOpts = rlRepresentationOptions('LearnRate',8e-3,'GradientThreshold',1);

% create the critic representation from the network
critic = rlValueRepresentation(criticNet,obsInfo,'Observation',{'state'},criticOpts);

PPO agents use a rlStochasticActorRepresentation. For continuous action spaces, stochastic actors can only use a neural network approximator.

The observation input (here called myobs) must accept a two-dimensional vector, as specified in obsInfo. The output (here called myact) must also be a two-dimensional vector (twice the number of dimensions specified in actInfo). The elements of the output vector represent, in sequence, all the means and all the standard deviations of every action (in this case there is only one mean value and one standard deviation).

The fact that standard deviations must be non-negative while mean values must fall within the output range means that the network must have two separate paths. The first path is for the mean values, and any output nonlinearity must be scaled so that it can produce outputs in the output range. The second path is for the standard deviations, and you must use a softplus or relu layer to enforce non-negativity.

% input path layers (2 by 1 input and a 1 by 1 output)
inPath = [ 
    imageInputLayer([obsInfo.Dimension 1], 'Normalization','none','Name','state')
    fullyConnectedLayer(10,'Name', 'ip_fc')  % 10 by 1 output
    reluLayer('Name', 'ip_relu')             % nonlinearity
    fullyConnectedLayer(1,'Name','ip_out') ];% 1 by 1 output

% path layers for mean value (1 by 1 input and 1 by 1 output)
% using scalingLayer to scale the range
meanPath = [
    fullyConnectedLayer(15,'Name', 'mp_fc1') % 15 by 1 output
    reluLayer('Name', 'mp_relu')             % nonlinearity
    fullyConnectedLayer(1,'Name','mp_fc2');  % 1 by 1 output
    tanhLayer('Name','tanh');                % output range: (-1,1)
    scalingLayer('Name','mp_out','Scale',actInfo.UpperLimit) ]; % output range: (-2N,2N)

% path layers for standard deviation (1 by 1 input and output)
% using softplus layer to make it non negative
sdevPath = [
    fullyConnectedLayer(15,'Name', 'vp_fc1') % 15 by 1 output
    reluLayer('Name', 'vp_relu')             % nonlinearity
    fullyConnectedLayer(1,'Name','vp_fc2');  % 1 by 1 output
    softplusLayer('Name', 'vp_out') ];       % output range: (0,+Inf)

% conctatenate two inputs (along dimension #3) to form a single (2 by 1) output layer
outLayer = concatenationLayer(3,2,'Name','mean&sdev');

% add layers to layerGraph network object
actorNet = layerGraph(inPath);
actorNet = addLayers(actorNet,meanPath);
actorNet = addLayers(actorNet,sdevPath);
actorNet = addLayers(actorNet,outLayer);

% connect layers: the mean value path output MUST be connected to the FIRST input of the concatenation layer
actorNet = connectLayers(actorNet,'ip_out','mp_fc1/in');   % connect output of inPath to meanPath input
actorNet = connectLayers(actorNet,'ip_out','vp_fc1/in');   % connect output of inPath to sdevPath input
actorNet = connectLayers(actorNet,'mp_out','mean&sdev/in1');% connect output of meanPath to mean&sdev input #1
actorNet = connectLayers(actorNet,'vp_out','mean&sdev/in2');% connect output of sdevPath to mean&sdev input #2

% plot network 
plot(actorNet)

Specify some options for the actor and create the stochastic actor representation using the deep neural network actorNet.

% set some training options for the actor
actorOpts = rlRepresentationOptions('LearnRate',8e-3,'GradientThreshold',1);

% create the actor using the network
actor = rlStochasticActorRepresentation(actorNet,obsInfo,actInfo,...
    'Observation',{'state'},actorOpts);

Specify agent options, and create a PPO agent using actor, critic and agent options.

agentOpts = rlPPOAgentOptions(...
    'ExperienceHorizon',1024, ...
    'DiscountFactor',0.95);
agent = rlPPOAgent(actor,critic,agentOpts)
agent = 
  rlPPOAgent with properties:

    AgentOptions: [1x1 rl.option.rlPPOAgentOptions]

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

getAction(agent,{rand(2,1)})
ans = 1x1 cell array
    {[0.6668]}

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

Create an environment and obtain observation and action information.

env = rlPredefinedEnv('CartPole-Discrete');
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);
numObs = obsInfo.Dimension(1);
numDiscreteAct = numel(actInfo.Elements);

Create a recurrent deep neural network for the critic. To create a recurrent neural network, use a sequenceInputLayer as the input layer and include an lstmLayer as one of the other network layers.

criticNetwork = [
    sequenceInputLayer(numObs,'Normalization','none','Name','state')
    fullyConnectedLayer(8, 'Name', 'fc')
    reluLayer('Name','relu')
    lstmLayer(8,'OutputMode','sequence','Name','lstm')
    fullyConnectedLayer(1,'Name','output')];

Create a value function representation object for the critic.

criticOptions = rlRepresentationOptions('LearnRate',1e-2,'GradientThreshold',1);
critic = rlValueRepresentation(criticNetwork,obsInfo,...
    'Observation','state', criticOptions);

Similarly, define a recurrent neural network for the actor.

actorNetwork = [
    sequenceInputLayer(numObs,'Normalization','none','Name','state')
    fullyConnectedLayer(8,'Name','fc')
    reluLayer('Name','relu')
    lstmLayer(8,'OutputMode','sequence','Name','lstm')
    fullyConnectedLayer(numDiscreteAct,'Name','output')
    softmaxLayer('Name','actionProb')];

Create a stochastic actor representation for the network.

actorOptions = rlRepresentationOptions('LearnRate',1e-3,'GradientThreshold',1);
actor = rlStochasticActorRepresentation(actorNetwork,obsInfo,actInfo,...
    'Observation','state', actorOptions);

Create a PPO agent using the actor and critic representations.

agentOptions = rlPPOAgentOptions(...
    'AdvantageEstimateMethod', 'finite-horizon', ...
    'ClipFactor', 0.1);
agent = rlPPOAgent(actor,critic,agentOptions);

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.

Introduced in R2019b