Environments
Model the dynamics and output of a reinforcement learning environment
In a reinforcement learning scenario, the environment models the world with which the agent interacts.
Reinforcement Learning Toolbox™ provides predefined objects that implement different benchmark environments. You can also create your own environments using custom functions for the environment dynamics, modifying an existing environment template class, or using a Simulink® model.
For an introduction to reinforcement learning environments, see Reinforcement Learning Environments.
Functions
Blocks
RL Agent | Reinforcement learning agent |
Topics
Introduction to Reinforcement Learning Environments
- Reinforcement Learning Environments
Model environment dynamics using a MATLAB® object that generates rewards and observations in response to agents actions.
Grid World Environments
- Load Predefined Grid World Environments
Load grid world environments in which the actions, observations, and rewards are already defined. - Create Custom Grid World Environments
Create custom grid world environments by defining your own grid size, rewards and obstacles.
Predefined Control System Environments
- Load Predefined Control System Environments
Load predefined environments used as benchmarks for control systems design.
Custom MATLAB Environments
- Define Reward and Observation Signals in Custom Environments
Create a reward signal that measures how successfully the agent actions are achieving a goal. - Create Custom Environment Using Step and Reset Functions
Create reinforcement learning environments by supplying custom step and reset functions. - Create Custom Environment from Class Template
Create a custom reinforcement learning environment by modifying a template environment class.
Custom Simulink Environments
- Define Reward and Observation Signals in Custom Environments
Create a reward signal that measures how successfully the agent actions are achieving a goal. - Create Custom Simulink Environments
Create a custom environment using a Simulink model that generates rewards and observations in response to agents actions. - Create and Simulate Same Environment in Both MATLAB and Simulink
Understand differences between reinforcement learning loops implemented in MATLAB and Simulink. - 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.
Load Environments in Reinforcement Learning Designer
- Load MATLAB Environments in Reinforcement Learning Designer
Load a MATLAB environment in the reinforcement designer app. - Load Simulink Environments in Reinforcement Learning Designer
Load a Simulink environment in the reinforcement designer app.