# 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

### Syntax

``opt = rlACAgentOptions``
``opt = rlACAgentOptions(Name,Value)``

### 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

expand all

Number of steps the agent interacts with the environment before learning from its experience, specified as a positive integer. When the agent uses a recurrent neural network, `NumStepsToLookAhead` is treated as the training trajectory length.

Entropy loss weight, specified as a scalar value between `0` and `1`. 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.

For episode step t, the entropy loss function, which is added to the loss function for actor updates, 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|θμ) 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.

Option to return the action with maximum likelihood for simulation and policy generation, specified as a logical value. When `UseDeterministicExploitation` is set to `true`, the action with maximum likelihood is always used in `sim` and `generatePolicyFunction`, which casues the agent to behave deterministically.

When `UseDeterministicExploitation` is set to `false`, the agent samples actions from probability distributions, which causes the agent to behave stochastically.

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

collapse all

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

`opt = rlACAgentOptions('DiscountFactor',0.95)`
```opt = rlACAgentOptions with properties: NumStepsToLookAhead: 32 EntropyLossWeight: 0 UseDeterministicExploitation: 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

expand all

Behavior change in future release