万博1manbetxEnvironments
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, seeCreate Simulink Reinforcement Learning Environments.
Functions
Blocks
RL Agent | Reinforcement learning agent |
Topics
- 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 Simulink Environments for 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
创建一个增强学习模拟环境,该环境包含一个RL代理块,代替储罐中水万博1manbetx位的控制器。