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.
|Create a predefined reinforcement learning environment|
|Create reinforcement learning environment using dynamic model implemented in Simulink|
|Create Simulink model for reinforcement learning, using reference model as environment|
|Validate custom reinforcement learning environment|
|Reinforcement learning environment with a dynamic model implemented in Simulink|
|Generate a reward function from control specifications to train a reinforcement learning agent|
|Exterior penalty value for a point with respect to a bounded region|
|Hyperbolic penalty value for a point with respect to a bounded region|
|Logarithmic barrier penalty value for a point with respect to a bounded region|
|Create discrete action or observation data specifications for reinforcement learning environments|
|Create continuous action or observation data specifications for reinforcement learning environments|
|Obtain action data specifications from reinforcement learning environment or agent|
|Obtain observation data specifications from reinforcement learning environment or agent|
|Create reinforcement learning data specifications for elements of a Simulink bus|
|RL Agent||Reinforcement learning agent|
Model environment dynamics using a Simulink model that interacts with the agent, generating rewards and observations in response to agent actions.
Import a custom Simulink environment or create a predefined Simulink environment.
Create a reward signal that measures how successful the agent is at achieving its goal.
Load predefined Simulink control system environments.
Create a reinforcement learning Simulink environment that contains an RL Agent block in place of a controller for the water level in a tank.