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training and validation -凯发k8网页登录

train and simulate reinforcement learning agents

to learn an optimal policy, a reinforcement learning agent interacts with the environment through a repeated trial-and-error process. during training, the agent tunes the parameters of its policy representation to maximize the long-term reward. reinforcement learning toolbox™ software provides functions for training agents and validating the training results through simulation. for more information, see train reinforcement learning agents.

apps

reinforcement learning designerdesign, train, and simulate reinforcement learning agents

functions

traintrain reinforcement learning agents within a specified environment
rltrainingoptionsoptions for training reinforcement learning agents
rlmultiagenttrainingoptionsoptions for training multiple reinforcement learning agents
trainfromdatatrain off-policy reinforcement learning agent using existing data
rltrainingfromdataoptionsoptions to train reinforcement learning agents using existing data
inspecttrainingresultplot training information from a previous training session
rldataloggercreate either a file logger object or a monitor logger object to log training data
rldatavieweropen reinforcement learning data viewer tool
fileloggerlog reinforcement learning training data to mat files
monitorloggerlog reinforcement learning training data to monitor window
trainingprogressmonitormonitor and plot training progress for deep learning custom training loops
setupset up reinforcement learning environment or initialize data logger object
storestore data in the internal memory of a (file or monitor) logger object
writetransfer stored data from the internal logger memory to the logging target
cleanupclean up reinforcement learning environment or data logger object
simsimulate trained reinforcement learning agents within specified environment
rlsimulationoptionsoptions for simulating a reinforcement learning agent within an environment
runepisodesimulate reinforcement learning environment against policy or agent
setupset up reinforcement learning environment or initialize data logger object
cleanupclean up reinforcement learning environment or data logger object
futureobject that supports deferred outputs for reinforcement learning environment simulations running on workers
fetchnextretrieve next available unread outputs from a reinforcement learning environment simulations running on workers
fetchoutputsretrieve results from all reinforcement learning environment simulations running on workers
cancelcancel unfinished reinforcement learning environment simulations on workers
waitwait for reinforcement learning environment simulations running on a workers to finish

blocks

rl agentreinforcement learning agent
policyreinforcement learning policy

topics

training and simulation basics

use the reinforcement learning designer app

use multiple processes and gpus

multi-agent training

train agents to control double integrator system

  • train ddpg agent to control double integrator system
    train a ddpg agent to control a second-order dynamic system modeled in matlab and compare it to an lqr controller.

  • train a discrete action space pg agent with a baseline to control a double integrator system modeled in matlab.

train agents to balance cart-pole system

train agents to swing up and balance pendulum

train agents to perform control tasks

  • tune pi controller using reinforcement learning
    tune the gains of a pi controller using a td3 agent.
  • train sac agent for ball balance control
    train a sac agent to balance a ball on a flat surface using a robot arm.

  • train sac and ppo agents to balance the quanser qube rotational inverted pendulum.

  • train a td3 agent to control the currents in a permanent magnet synchronous motor.

  • train a dqn agent with a recurrent network to control the temperature of an house.

  • train a ddpg agent with actions constrained using the constraint enforcement block.

train agents to control robots

generate rewards from control specifications

imitation learning


  • train a deep neural network to imitate the behavior of a model predictive controller within a lane keeping assist system.

  • train a deep neural network to imitate the behavior of a nonlinear model predictive controller for a flying robot.

  • train a ddpg agent using an actor network that has been previously trained using supervised learning.

train agents for automotive applications

  • train dqn agent for lane keeping assist
    train a dqn agent for a lane keeping assist application.

  • train a ddpg agent for an adaptive cruise control application.

  • train a ddpg agent for a lane following application.

  • train a discrete action space ppo agent to park a car in an open parking space.

other applications


  • this example shows how to use the reinforcement learning toolbox™ and deep learning toolbox™ to design agents for optimal trade execution.

  • train a deep q-network (dqn) reinforcement learning agent for beam selection in a 5g new radio communications system.
  • water distribution system scheduling using reinforcement learning
    train a dqn agent to optimally activate pumps in a water distribution system.

develop custom agents and training algorithms

deploy agents and policies


  • verify a reinforcement learning agent in software-in-the-loop and processor-in-the-loop modes.

  • generate a policy block to deploy a trained policy.
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