applications -凯发k8网页登录

tutorials

train agents to balance cart-pole system

• train dqn agent to balance cart-pole system
  train a dqn agent to balance a cart-pole system modeled in matlab^®.
• 
  train a pg agent to balance a discrete action space cart-pole system modeled in matlab.
• train ac agent to balance cart-pole system
  train a ac agent to balance a discrete action space cart-pole system modeled in matlab.
• 
  train a ddpg agent to swing up and balance a cart-pole system modeled in simscape™ multibody™.
• train mbpo agent to balance cart-pole system
  a model-based reinforcement learning agent learns a model of its environment that it can use to generate additional experiences for training.

train agents to swing up and balance pendulum

• train dqn agent to swing up and balance pendulum
  train a dqn agent to swing up and balance a pendulum modeled in simulink^®.
• 
  train a ddpg agent to balance a pendulum modeled in simulink.
• 
  train a ddpg agent to balance a pendulum simulink model that contains observations in a bus signal.
• train ddpg agent to swing up and balance pendulum with image observation
  train a ddpg agent using an image-based observation signal.
• create dqn agent using deep network designer and train using image observations
  create a reinforcement learning agent using the deep network designer app from the deep learning toolbox™.

train agents to control double integrator system

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

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.
• create and train custom lqr agent
  create a custom agent that solves an lqr problem and train it using the built-in train function.

train agents to control robots

• train ddpg agent to control sliding robot
  train a ddpg agent to control a flying robot model.
• 
  train a ppo agent to land a discrete action space flying robot.
• train biped robot to walk using reinforcement learning agents
  compare ddpg and td3 agent for the control a biped walking robot modeled in simscape multibody.

generate rewards from control specifications

• generate reward function from a model predictive controller for a servomotor
  generate a reward function from an mpc controller applied to a servomotor and use it to train a td3 agent.
• generate reward function from a model verification block for a water tank system
  generate a reward function from an model verification block applied to a water tank system and use it to train a td3 agent.

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.
• 
  this example shows a reinforcement learning (rl) approach to maximize the probability of obtaining an investor's wealth goal at the end of the investment horizon.
• 
  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.

train model based policy optimization agents

• train mbpo agent to balance cart-pole system
  a model-based reinforcement learning agent learns a model of its environment that it can use to generate additional experiences for training.
• model-based reinforcement learning using custom training loop
  create a model-based reinforcement learning agent using a custom training loop.