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 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:
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.
UseDeterministicExploitation— Use action with maximum likelihood
Option to return the action with maximum likelihood for simulation and policy generation,
specified as a logical value. When
true, the action with maximum likelihood is always used in
generatePolicyFunction, which casues the agent to behave
UseDeterministicExploitation is set to
agent samples actions from probability distributions, which causes the agent to behave
SampleTime— Sample time of agent
1(default) | positive scalar
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
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 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
opt.SampleTime = 0.5;
NumStepsToLookAheadchanged 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.