setActor
Set actor of reinforcement learning agent
Description
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
rlPGAgent
| rlDDPGAgent
| rlTD3Agent
| rlACAgent
| rlSACAgent
| rlPPOAgent
| rlTRPOAgent
Reinforcement learning agent that contains an actor, specified as one of the following:
rlPGAgent
objectrlDDPGAgent
objectrlTD3Agent
objectrlACAgent
objectrlSACAgent
objectrlPPOAgent
objectrlTRPOAgent
object
Note
agent
is an handle object. Therefore is updated by
setActor
whether agent
is returned as an
output argument or not. For more information about handle objects, see Handle Object Behavior.
Note
if agent
is an rlMBPOAgent
object, to set the actor, use
setActor(agent.BaseAgent,actor)
.
actor
— Actor
rlContinuousDeterministicActor
object | rlDiscreteCategoricalActor
object | rlContinuousGaussianActor
object
rlContinuousDeterministicActor
rlDiscreteCategoricalActor
rlContinuousGaussianActor
Actor object, specified as one of the following:
rlContinuousDeterministicActor
object — Specify whenagent
is anrlDDPGAgent
orrlTD3Agent
objectrlDiscreteCategoricalActor
object — Specify whenagent
is anrlACAgent
,rlPGAgent
,rlPPOAgent
,rlTRPOAgent
orrlSACAgent
object for an environment with a discrete action space.rlContinuousGaussianActor
object — Specify whenagent
is anrlACAgent
,rlPGAgent
,rlPPOAgent
,rlTRPOAgent
orrlSACAgent
object for an environment with a continuous action space.
The input and outputs of the approximation model in the actor (typically, a neural network) must match the observation and action specifications of the original agent.
To create an actor, use one of the following methods:
Create the actor using the corresponding function approximator object.
Obtain the existing actor from an agent using
getActor
.
Output Arguments
agent
— Updated reinforcement learning agent
rlPGAgent
| rlDDPGAgent
| rlTD3Agent
| rlACAgent
| rlSACAgent
| rlPPOAgent
| rlTRPOAgent
Updated agent, returned as an agent object. Note that agent
is
an handle object. Therefore its actor is updated by setActor
whether agent
is returned as an output argument or not. For more
information about handle objects, see Handle Object Behavior.
Version History
Introduced in R2019a
See Also
Functions
MATLAB 명령
다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.
명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.
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