getActor

Extract actor from reinforcement learning agent

Syntax

actor = getActor(agent)

Description

actor = getActor(agent) returns the actor object from the specified reinforcement learning agent.

Examples

Modify Actor Parameter Values

Assume that you have an existing trained reinforcement learning agent. For this example, load the trained agent from Compare DDPG Agent to LQR Controller.

load("DoubleIntegDDPG.mat","agent") 

Obtain the actor function approximator from the agent.

actor = getActor(agent);

For approximator objects, you can access the Learnables property using dot notation.

First, display the parameters.

actor.Learnables{1}

ans = 
  1x2 single dlarray

  -15.4663   -7.2746

Modify the parameter values. For this example, simply divide all of the parameters by 2.

actor.Learnables{1} = actor.Learnables{1}/2;

Display the new parameters.

actor.Learnables{1}

ans = 
  1x2 single dlarray

   -7.7331   -3.6373

Alternatively, you can use getLearnableParameters and setLearnableParameters.

Obtain the learnable parameters from the actor.

params = getLearnableParameters(actor)

params=2×1 cell array
    {[-7.7331 -3.6373]}
    {[              0]}

Modify the parameter values. For this example, simply multiply all of the parameters by 2.

modifiedParams = cellfun(@(x) x*2,params,"UniformOutput",false);

Set the parameter values of the actor to the new modified values.

actor = setLearnableParameters(actor,modifiedParams);

Set the actor in the agent to the new modified actor.

setActor(agent,actor);

Display the new parameter values.

getLearnableParameters(getActor(agent))

ans=2×1 cell array
    {[-15.4663 -7.2746]}
    {[               0]}

Modify Deep Neural Networks in Reinforcement Learning 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.

Load the predefined environment.

env = rlPredefinedEnv("DoubleIntegrator-Continuous");

Obtain observation and action specifications.

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

Create a PPO agent from the environment observation and action specifications. This agent uses default deep neural networks for its actor and critic.

agent = rlPPOAgent(obsInfo,actInfo);

To modify the deep neural networks within a reinforcement learning agent, you must first extract the actor and critic function approximators.

actor = getActor(agent);
critic = getCritic(agent);

Extract the deep neural networks from both the actor and critic function approximators.

actorNet = getModel(actor);
criticNet = getModel(critic);

Plot the actor network.

plot(actorNet)

To validate a network, use analyzeNetwork. For example, validate the critic network.

analyzeNetwork(criticNet)

You can modify the actor and critic networks and save them back to the agent. To modify the networks, you can use the Deep Network Designer app. To open the app for each network, use the following commands.

deepNetworkDesigner(criticNet)
deepNetworkDesigner(actorNet)

In Deep Network Designer, modify the networks. For example, you can add additional layers to your network. When you modify the networks, do not change the input and output layers of the networks returned by getModel. For more information on building networks, see Build Networks with Deep Network Designer.

To validate the modified network in Deep Network Designer, you must click on Analyze, under the Analysis section. To export the modified network structures to the MATLAB® workspace, generate code for creating the new networks and run this code from the command line. Do not use the exporting option in Deep Network Designer. For an example that shows how to generate and run code, see Create DQN Agent Using Deep Network Designer and Train Using Image Observations.

For this example, the code for creating the modified actor and critic networks is in the createModifiedNetworks helper script.

createModifiedNetworks

Each of the modified networks includes an additional fullyConnectedLayer and reluLayer in their main common path. Plot the modified actor network.

plot(modifiedActorNet)

After exporting the networks, insert the networks into the actor and critic function approximators.

actor = setModel(actor,modifiedActorNet);
critic = setModel(critic,modifiedCriticNet);

Finally, insert the modified actor and critic function approximators into the actor and critic objects.

agent = setActor(agent,actor);
agent = setCritic(agent,critic);

Input Arguments

agent — Reinforcement learning agent

rlDDPGAgent object | rlTD3Agent object | rlPGAgent object | rlACAgent object | rlPPOAgent object | rlSACAgent object

Reinforcement learning agent that contains an actor, specified as one of the following:

• rlPGAgent object

• rlDDPGAgent object

• rlTD3Agent object

• rlACAgent object

• rlSACAgent object

• rlPPOAgent object

• rlTRPOAgent object

Note

if agent is an rlMBPOAgent object, to extract the actor, use getActor(agent.BaseAgent).

Output Arguments

actor — Actor

rlContinuousDeterministicActor object | rlDiscreteCategoricalActor object | rlContinuousGaussianActor object

Actor object, returned as one of the following:

• rlContinuousDeterministicActor object — Returned when agent is an rlDDPGAgent or rlTD3Agent object.

• rlDiscreteCategoricalActor object — Returned when agent is an rlACAgent, rlPGAgent, rlPPOAgent, rlTRPOAgent or rlSACAgent object with a discrete action space.

• rlContinuousGaussianActor object — Returned when agent is an rlACAgent, rlPGAgent, rlPPOAgent, rlTRPOAgent or rlSACAgent object with a continuous action space.

• rlHybridStochasticActor object — Returned when agent is an rlSACAgent object with an hybrid action space.

Version History

Introduced in R2019a