Options for proximal policy optimization reinforcement learning agent
rlPPOAgentOptions object to specify options for
proximal policy optimization (PPO) agents. To create a PPO agent, use
For more information on PPO agents, see Proximal Policy Optimization Agents.
For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.
opt = rlPPOAgentOptions
rlPPOAgentOptions object for use as an argument when creating a PPO
agent using all default settings. You can modify the object properties using dot
ExperienceHorizon— Number of steps the agent interacts with the environment before learning
512(default) | positive integer
Number of steps the agent interacts with the environment before learning from its experience, specified as a positive integer.
ExperienceHorizon value must be greater than or equal to
ClipFactor— Clip factor
0.2(default) | positive scalar less than
Clip factor for limiting the change in each policy update step, specified as a
positive scalar less than
EntropyLossWeight— Entropy loss weight
0.01(default) | scalar value greater
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.
MiniBatchSize— Mini-batch size
128(default) | positive integer
Mini-batch size used for each learning epoch, specified as a positive integer.
MiniBatchSize value must be less than or equal to the
NumEpoch— Number of epochs
3(default) | positive integer
Number of epochs for which the actor and critic networks learn from the current experience set, specified as a positive integer.
AdvantageEstimateMethod— Method for estimating advantage values
Method for estimating advantage values, specified as one of the following:
"gae" — Generalized advantage estimator
"finite-horizon" — Finite horizon estimation
For more information on these methods, see the training algorithm information in Proximal Policy Optimization Agents.
GAEFactor— Smoothing factor for generalized advantage estimator
0.95(default) | scalar value between
Smoothing factor for generalized advantage estimator, specified as a scalar value
1, inclusive. This option applies
only when the
AdvantageEstimateMethod option is
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.
|Proximal policy optimization reinforcement learning agent|
Create a PPO agent options object, specifying the experience horizon.
opt = rlPPOAgentOptions('ExperienceHorizon',256)
opt = rlPPOAgentOptions with properties: ExperienceHorizon: 256 MiniBatchSize: 128 ClipFactor: 0.2000 EntropyLossWeight: 0.0100 NumEpoch: 3 AdvantageEstimateMethod: "gae" GAEFactor: 0.9500 SampleTime: 1 DiscountFactor: 0.9900
You can modify options using dot notation. For example, set the agent sample time to
opt.SampleTime = 0.5;