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rlPPOAgentOptions

Options for PPO agent

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 Agents.

For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.

Creation

Description

opt = rlPPOAgentOptions creates an rlPPOAgentOptions object for use as an argument when creating a PPO agent using all default settings. You can modify the object properties using dot notation.

example

opt = rlPPOAgentOptions(Name,Value) sets option properties using name-value arguments. For example, 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

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Number of steps the agent interacts with the environment before learning from its experience, specified as a positive integer.

The ExperienceHorizon value must be greater than or equal to the MiniBatchSize value.

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.

Clip factor for limiting the change in each policy update step, specified as a positive scalar less than 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.

Number of epochs for which the actor and critic networks learn from the current experience set, specified as a positive integer.

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 Agents.

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"

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 the AdvantageNormalizingWindow 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.

Window size for normalizing advantage function values, specified as a positive integer. Use this option when the NormalizedAdvantageMethod option is "moving".

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.

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.

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.

Discount factor applied to future rewards during training, specified as a positive scalar less than or equal to 1.

Object Functions

rlPPOAgentProximal policy optimization reinforcement learning agent

Examples

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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 R2019b

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