simulink environments -凯发k8网页登录

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 .

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

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 environments
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.