Options for AC agent
rlACAgentOptions object to specify options for
creating actor-critic (AC) agents. To create an actor-critic agent, use
For more information see Actor-Critic Agents.
For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.
creates a default
option set for an AC agent. You can modify the object properties using dot
opt = rlACAgentOptions
NumStepsToLookAhead — Number of steps ahead
32 (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 uses a recurrent neural
NumStepsToLookAhead is treated as the training trajectory
EntropyLossWeight — Entropy loss weight
0 (default) | scalar value between
Entropy loss weight, specified as a scalar value between
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.
ActorOptimizerOptions — Actor optimizer options
CriticOptimizerOptions — Critic optimizer options
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
SampleTime — Sample time of agent
1 (default) | positive scalar |
Sample time of agent, specified as a positive scalar or as
-1. Setting this
-1 allows for event-based simulations.
Within a Simulink® environment, the RL Agent block
in which the agent is specified to execute every
of simulation time. If
block inherits the sample time from its parent subsystem.
Within a MATLAB® environment, the agent is executed every time the environment advances. In
SampleTime is the time interval between consecutive
elements in the output experience returned by
-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.
|Actor-critic reinforcement learning agent|
Create AC Agent Options Object
Create an AC agent options object, specifying the discount factor.
opt = rlACAgentOptions('DiscountFactor',0.95)
opt = rlACAgentOptions with properties: NumStepsToLookAhead: 32 EntropyLossWeight: 0 ActorOptimizerOptions: [1x1 rl.option.rlOptimizerOptions] CriticOptimizerOptions: [1x1 rl.option.rlOptimizerOptions] SampleTime: 1 DiscountFactor: 0.9500 InfoToSave: [1x1 struct]
You can modify options using dot notation. For example, set the agent sample time to
opt.SampleTime = 0.5;
Version HistoryIntroduced in R2019a
R2020b: Default value for
NumStepsToLookAhead changed to 32
Behavior change in future release
A value of 32 for this property should work better than 1 for most environments. If you
nave MATLAB R2020b or a later version and you want
to reproduce how
rlACAgent behaved on
versions prior to R2020b, set this value to 1.