rlACAgentOptions

Options for AC agent

Description

Use an rlACAgentOptions object to specify options for creating actor-critic (AC) agents. To create an actor-critic agent, use rlACAgent

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

Creation

Description

opt = rlACAgentOptions creates a default option set for an AC agent. You can modify the object properties using dot notation.

example

opt = rlACAgentOptions(Name,Value) sets option properties using name-value pairs. For example, rlDQNAgentOptions('DiscountFactor',0.95) creates an option set with a discount factor of 0.95. You can specify multiple name-value pairs. Enclose each property name in quotes.

Properties

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Number of steps to look ahead in model training, specified as a positive integer. For AC agents, the number of steps to look ahead corresponds to the training episode length.

Entropy loss weight, specified as a scalar value between 0 and 1, inclusive. A higher 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.

The entropy loss function for episode step t is:

${H}_{t}=E\sum _{k=1}^{M}{\mu }_{k}\left({S}_{t}|{\theta }_{\mu }\right)\mathrm{ln}{\mu }_{k}\left({S}_{t}|{\theta }_{\mu }\right)$

Here:

• E is the entropy loss weight.

• M is the number of possible actions.

• μk(St) is the probability of taking action Ak when in state St following the current policy.

When gradients are computed during training, an additional gradient component is computed for minimizing this loss function.

Sample time of agent, specified as a positive scalar.

Within a Simulink environment, the agent gets executed every SampleTime seconds of simulation time.

Within a MATLAB environment, the agent gets executed every time the environment advances. However, SampleTime is the time interval between consecutive elements in the output experience returned by sim or train.

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

Object Functions

 rlACAgent Actor-critic reinforcement learning agent

Examples

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Create an AC agent options object, specifying the discount factor.

opt = rlACAgentOptions('DiscountFactor',0.95)
opt =
rlACAgentOptions with properties:

EntropyLossWeight: 0
SampleTime: 1
DiscountFactor: 0.9500

You can modify options using dot notation. For example, set the agent sample time to 0.5.

opt.SampleTime = 0.5;

Compatibility Considerations

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Behavior change in future release