rlReplayMemory
Replay memory experience buffer
Description
An off-policy reinforcement learning agent stores experiences in a circular experience buffer. During training, the agent samples mini-batches of experiences from the buffer and uses these mini-batches to update its actor and critic function approximators.
By default, built-in off-policy agents (DQN, DDPG, TD3, SAC, MBPO) use an
rlReplayMemory
object as their experience buffer. Agents uniformly
sample data from this buffer. To perform nonuniform prioritized sampling, use an rlPrioritizedReplayMemory
object.
When you create a custom off-policy reinforcement learning agent, you can create an
experience buffer by using an rlReplayMemory
object.
Creation
Description
Input Arguments
Properties
Object Functions
append | Append experiences to replay memory buffer |
sample | Sample experiences from replay memory buffer |
resize | Resize replay memory experience buffer |
allExperiences | Return all experiences in replay memory buffer |
getActionInfo | Obtain action data specifications from reinforcement learning environment, agent, or experience buffer |
getObservationInfo | Obtain observation data specifications from reinforcement learning environment, agent, or experience buffer |
Examples
Version History
Introduced in R2022a