rlPPOAgentOptions
Description
Use an rlPPOAgentOptions
object to specify options for proximal
policy optimization (PPO) agents. To create a PPO agent, use rlPPOAgent
.
For more information on PPO agents, see Proximal Policy Optimization (PPO) Agents.
For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.
Creation
Description
creates an
opt
= rlPPOAgentOptionsrlPPOAgentOptions
object for use as an argument when creating a PPO
agent using all default settings. You can modify the object properties using dot
notation.
sets option properties using
name-value arguments. For example,
opt
= rlPPOAgentOptions(Name,Value
)rlPPOAgentOptions('DiscountFactor',0.95)
creates an option set with a
discount factor of 0.95
. You can specify multiple name-value arguments.
Enclose each property name in quotes.
Properties
ExperienceHorizon
— Number of steps the agent interacts with the environment before learning
512
(default) | positive integer
Number of steps the agent interacts with the environment before learning from its
experience, specified as a positive integer. When the agent is trained in parallel,
ExperienceHorizon
is ignored, and the whole episode is used to
compute the gradients.
The ExperienceHorizon
value must be greater than or equal to
the MiniBatchSize
value.
MiniBatchSize
— Mini-batch size
128
(default) | positive integer
Mini-batch size used for each learning epoch, specified as a positive integer. When the agent uses a recurrent neural network, MiniBatchSize
is treated as the training trajectory length.
The MiniBatchSize
value must be less than or equal to the ExperienceHorizon
value.
ClipFactor
— Clip factor
0.2
(default) | positive scalar less than 1
Clip factor for limiting the change in each policy update step, specified as a
positive scalar less than 1
.
EntropyLossWeight
— Entropy loss weight
0.01
(default) | scalar value between 0
and 1
Entropy loss weight, specified as a scalar value between 0
and
1
. A higher entropy loss weight value promotes agent exploration by
applying a penalty for being too certain about which action to take. Doing so can help
the agent move out of local optima.
When gradients are computed during training, an additional gradient component is computed for minimizing this loss function. For more information, see Entropy Loss.
NumEpoch
— Number of epochs
3
(default) | positive integer
Number of epochs for which the actor and critic networks learn from the current experience set, specified as a positive integer.
AdvantageEstimateMethod
— Method for estimating advantage values
"gae"
(default) | "finite-horizon"
Method for estimating advantage values, specified as one of the following:
"gae"
— Generalized advantage estimator"finite-horizon"
— Finite horizon estimation
For more information on these methods, see the training algorithm information in Proximal Policy Optimization (PPO) Agents.
GAEFactor
— Smoothing factor for generalized advantage estimator
0.95
(default) | scalar value between 0
and 1
Smoothing factor for generalized advantage estimator, specified as a scalar value between 0
and 1
, inclusive. This option applies only when the AdvantageEstimateMethod
option is "gae"
NormalizedAdvantageMethod
— Method for normalizing advantage function
"none"
(default) | "current
| "moving"
Method for normalizing advantage function values, specified as one of the following:
"none"
— Do not normalize advantage values"current"
— Normalize the advantage function using the mean and standard deviation for the current mini-batch of experiences."moving"
— Normalize the advantage function using the mean and standard deviation for a moving window of recent experiences. To specify the window size, set theAdvantageNormalizingWindow
option.
In some environments, you can improve agent performance by normalizing the advantage function during training. The agent normalizes the advantage function by subtracting the mean advantage value and scaling by the standard deviation.
AdvantageNormalizingWindow
— Window size for normalizing advantage function
1e6
(default) | positive integer
Window size for normalizing advantage function values, specified as a positive integer. Use this option when the NormalizedAdvantageMethod
option is "moving"
.
ActorOptimizerOptions
— Actor optimizer options
rlOptimizerOptions
object
Actor optimizer options, specified as an rlOptimizerOptions
object. It allows you to specify training parameters of
the actor approximator such as learning rate, gradient threshold, as well as the
optimizer algorithm and its parameters. For more information, see rlOptimizerOptions
and rlOptimizer
.
CriticOptimizerOptions
— Critic optimizer options
rlOptimizerOptions
object
Critic optimizer options, specified as an rlOptimizerOptions
object. It allows you to specify training parameters of
the critic approximator such as learning rate, gradient threshold, as well as the
optimizer algorithm and its parameters. For more information, see rlOptimizerOptions
and rlOptimizer
.
SampleTime
— Sample time of agent
1
(default) | 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.
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.
DiscountFactor
— Discount factor
0.99
(default) | positive scalar less than or equal to 1
Discount factor applied to future rewards during training, specified as a positive scalar less than or equal to 1.
Object Functions
rlPPOAgent | Proximal policy optimization (PPO) reinforcement learning agent |
Examples
Create PPO Agent Options Object
Create a PPO agent options object, specifying the experience horizon.
opt = rlPPOAgentOptions(ExperienceHorizon=256)
opt = rlPPOAgentOptions with properties: ExperienceHorizon: 256 MiniBatchSize: 128 ClipFactor: 0.2000 EntropyLossWeight: 0.0100 NumEpoch: 3 AdvantageEstimateMethod: "gae" GAEFactor: 0.9500 NormalizedAdvantageMethod: "none" AdvantageNormalizingWindow: 1000000 ActorOptimizerOptions: [1x1 rl.option.rlOptimizerOptions] CriticOptimizerOptions: [1x1 rl.option.rlOptimizerOptions] SampleTime: 1 DiscountFactor: 0.9900 InfoToSave: [1x1 struct]
You can modify options using dot notation. For example, set the agent sample time to 0.5
.
opt.SampleTime = 0.5;
Version History
Introduced in R2019bR2022a: Simulation and deployment: UseDeterministicExploitation
will be removed
The property UseDeterministicExploitation
of the
rlPPOAgentOptions
object will be removed in a future release. Use the
UseExplorationPolicy
property of rlPPOAgent
instead.
Previously, you set UseDeterministicExploitation
as follows.
Force the agent to always select the action with maximum likelihood, thereby using a greedy deterministic policy for simulation and deployment.
agent.AgentOptions.UseDeterministicExploitation = true;
Allow the agent to select its action by sampling its probability distribution for simulation and policy deployment, thereby using a stochastic policy that explores the observation space.
agent.AgentOptions.UseDeterministicExploitation = false;
Starting in R2022a, set UseExplorationPolicy
as follows.
Force the agent to always select the action with maximum likelihood, thereby using a greedy deterministic policy for simulation and deployment.
agent.UseExplorationPolicy = false;
Allow the agent to select its action by sampling its probability distribution for simulation and policy deployment, thereby using a stochastic policy that explores the observation space.
agent.UseExplorationPolicy = true;
Similarly to UseDeterministicExploitation
,
UseExplorationPolicy
affects only simulation and deployment; it does
not affect training.
MATLAB 명령
다음 MATLAB 명령에 해당하는 링크를 클릭했습니다.
명령을 실행하려면 MATLAB 명령 창에 입력하십시오. 웹 브라우저는 MATLAB 명령을 지원하지 않습니다.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)