rlPredefinedEnv

Create a predefined reinforcement learning environment

Syntax

env = rlPredefinedEnv(keyword)

Description

env = rlPredefinedEnv(keyword) takes a predefined keyword keyword representing the environment name to create a MATLAB^® or Simulink^® reinforcement learning environment env. The environment env models the dynamics with which the agent interacts, generating rewards and observations in response to agent actions.

Examples

Basic Grid World Reinforcement Learning Environment

Use the predefined "BasicGridWorld" keyword to create a basic grid world reinforcement learning environment.

env = rlPredefinedEnv("BasicGridWorld")

env = 
  rlMDPEnv with properties:

       Model: [1×1 rl.env.GridWorld]
    ResetFcn: []

Continuous Double-Integrator Reinforcement Learning Environment

Use the predefined "DoubleIntegrator-Continuous" keyword to create a continuous double-integrator reinforcement learning environment.

env = rlPredefinedEnv("DoubleIntegrator-Continuous")

env = 
  DoubleIntegratorContinuousAction with properties:

             Gain: 1
               Ts: 0.1000
      MaxDistance: 5
    GoalThreshold: 0.0100
                Q: [2×2 double]
                R: 0.0100
         MaxForce: Inf
            State: [2×1 double]

You can visualize the environment using the plot function and interact with it using the reset and step functions.

plot(env)
observation = reset(env)

observation = 2×1

     4
     0

[observation,reward,isDone] = step(env,16)

observation = 2×1

    4.0800
    1.6000

reward = 
-16.5559

isDone = logical
   0

Create Continuous Simple Pendulum Model Environment

Use the predefined "SimplePendulumModel-Continuous" keyword to create a continuous simple pendulum model reinforcement learning environment.

env = rlPredefinedEnv("SimplePendulumModel-Continuous")

env = 
SimulinkEnvWithAgent with properties:

           Model : rlSimplePendulumModel
      AgentBlock : rlSimplePendulumModel/RL Agent
        ResetFcn : []
  UseFastRestart : on

Input Arguments

keyword — Predefined keyword representing the environment name

string | character vector | ...

Predefined keyword representing the environment name, specified as one of the following:

MATLAB Environment

• "BasicGridWorld"

• "CartPole-Discrete"

• "CartPole-Continuous"

• "DoubleIntegrator-Discrete"

• "DoubleIntegrator-Continuous"

• "SimplePendulumWithImage-Discrete"

• "SimplePendulumWithImage-Continuous"

• "WaterFallGridWorld-Stochastic"

• "WaterFallGridWorld-Deterministic"

Simulink Environment

• "SimplePendulumModel-Discrete"

• "SimplePendulumModel-Continuous"

• "CartPoleSimscapeModel-Discrete"

• "CartPoleSimscapeModel-Continuous"

Output Arguments

env — MATLAB or Simulink environment object

rlMDPEnv object | CartPoleDiscreteAction object | CartPoleContinuousAction object | DoubleIntegratorDiscreteAction object | DoubleIntegratorContinuousAction object | SimplePendlumWithImageDiscreteAction object | SimplePendlumWithImageContinuousAction object | SimulinkEnvWithAgent object

MATLAB or Simulink environment object, returned as one of the following:

• rlMDPEnv object, when you use one of the following keywords.

  • "BasicGridWorld"

  • "WaterFallGridWorld-Stochastic"

  • "WaterFallGridWorld-Deterministic"

• CartPoleDiscreteAction object, when you use the "CartPole-Discrete" keyword.

• CartPoleContinuousAction object, when you use the "CartPole-Continuous" keyword.

• DoubleIntegratorDiscreteAction object, when you use the "DoubleIntegrator-Discrete" keyword.

• DoubleIntegratorContinuousAction object, when you use the "DoubleIntegrator-Continuous" keyword.

• SimplePendlumWithImageDiscreteAction object, when you use the "SimplePendulumWithImage-Discrete" keyword.

• SimplePendlumWithImageContinuousAction object, when you use the "SimplePendulumWithImage-Continuous" keyword.

• SimulinkEnvWithAgent object, when you use one of the following keywords.

  • "SimplePendulumModel-Discrete"

  • "SimplePendulumModel-Continuous"

  • "CartPoleSimscapeModel-Discrete"

  • "CartPoleSimscapeModel-Continuous"

Version History

Introduced in R2019a