Simulink Environments

Model reinforcement learning environment dynamics using Simulink^® models

In a reinforcement learning scenario, the environment models the dynamics with which the agent interacts. The environment:

Receives actions from the agent
Outputs observations resulting from the dynamic behavior of the environment model
Generates a reward measuring how well the action contributes to achieving the task

You can create predefined and custom environments using Simulink models. For more information, see Create Simulink Reinforcement Learning Environments.

Functions

`rlPredefinedEnv`	Create a predefined reinforcement learning environment
`rlSimulinkEnv`	Create reinforcement learning environment using dynamic model implemented in Simulink
`createIntegratedEnv`	Create Simulink model for reinforcement learning, using reference model as environment
`validateEnvironment`	Validate custom reinforcement learning environment
`SimulinkEnvWithAgent`	Reinforcement learning environment with a dynamic model implemented in Simulink

`generateRewardFunction`	Generate a reward function from control specifications to train a reinforcement learning agent
`exteriorPenalty`	Exterior penalty value for a point with respect to a bounded region
`hyperbolicPenalty`	Hyperbolic penalty value for a point with respect to a bounded region
`barrierPenalty`	Logarithmic barrier penalty value for a point with respect to a bounded region

`rlFiniteSetSpec`	Create discrete action or observation data specifications for reinforcement learning environments
`rlNumericSpec`	Create continuous action or observation data specifications for reinforcement learning environments
`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
`bus2RLSpec`	Create reinforcement learning data specifications for elements of a Simulink bus

`reset`	Reset environment, agent, experience buffer, or policy object
`setup`	Set up reinforcement learning environment or initialize data logger object
`cleanup`	Clean up reinforcement learning environment or data logger object

Reinforcement learning agent

Create Simulink Reinforcement Learning Environments
Model environment dynamics using a Simulink model that interacts with the agent, generating rewards and observations in response to agent actions.
Create or Import Simulink Environments in Reinforcement Learning Designer
Import a custom Simulink environment or create a predefined Simulink environment.
Define Reward Signals
Create a reward signal that measures how successful the agent is at achieving its goal.
Load Predefined Simulink Environments
Load predefined Simulink control system environments.
Water Tank Reinforcement Learning Environment Model
Create a reinforcement learning Simulink environment that contains an RL Agent block in place of a controller for the water level in a tank.