environments -凯发k8网页登录
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 (since r2019a) |
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. - 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.