Options for policy gradient agent
rlPGAgentOptions object to specify options for policy
gradient (PG) agents. To create a PG agent, use
For more information on PG agents, see Policy Gradient Agents.
For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.
opt = rlPGAgentOptions
rlPGAgentOptions object for use as an argument when creating a PG
agent using all default settings. You can modify the object properties using dot
UseBaseline— Use baseline for learning
Instruction to use baseline for learning, specified as a logical values. When
UseBaseline is true, you must specify a critic network as the
baseline function approximator.
In general, for simpler problems with smaller actor networks, PG agents work better without a baseline.
SampleTime— Sample time of agent
1(default) | positive scalar
Sample time of agent, specified as a positive scalar.
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.
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.
The entropy loss function for episode step t is:
E is the entropy loss weight.
M is the number of possible actions.
μk(St) is the probability of taking action Ak following the current policy.
When gradients are computed during training, an additional gradient component is computed for minimizing this loss function.
|Policy gradient reinforcement learning agent|
This example shows how to create and modify a PG agent options object.
Create a PG agent options object, specifying the discount factor.
opt = rlPGAgentOptions('DiscountFactor',0.9)
opt = rlPGAgentOptions with properties: UseBaseline: 1 EntropyLossWeight: 0 SampleTime: 1 DiscountFactor: 0.9000
You can modify options using dot notation. For example, set the agent sample time to
opt.SampleTime = 0.5;