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model reinforcement learning environment dynamics using matlab^®

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 in matlab. for more information, see .

functions

`rlpredefinedenv`	create a predefined reinforcement learning environment
`rlfunctionenv`	specify custom reinforcement learning environment dynamics using functions
`rlcreateenvtemplate`	create custom reinforcement learning environment template
`rlmdpenv`	create markov decision process environment for reinforcement learning
`createmdp`	create markov decision process model
`creategridworld`	create a two-dimensional grid world for reinforcement learning
`validateenvironment`	validate custom reinforcement learning environment

`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

`rlneuralnetworkenvironment`	environment model with deep neural network transition models
`rlcontinuousdeterministictransitionfunction`	deterministic transition function approximator object for neural network-based environment
`rlcontinuousgaussiantransitionfunction`	stochastic gaussian transition function approximator object for neural network-based environment
`rlcontinuousdeterministicrewardfunction`	deterministic reward function approximator object for neural network-based environment
`rlcontinuousgaussianrewardfunction`	stochastic gaussian reward function approximator object for neural network-based environment
`rlisdonefunction`	is-done function approximator object for neural network-based environment
`predict`	predict next observation, next reward, or episode termination given observation and action input data
`evaluate`	evaluate function approximator object given observation (or observation-action) input data
`accelerate`	option to accelerate computation of gradient for approximator object based on neural network

`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

`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

model environment dynamics using a matlab object that interacts with the agent, generating rewards and observations in response to agent actions.
import a custom matlab environment or create a predefined matlab environment.
create a reward signal that measures how successful the agent is at achieving its goal.
load predefined matlab control system environments.
load predefined grid world environments
train agents in predefined matlab grid world environments for which the actions, observations, and rewards are already defined.
create custom grid world environments
create custom matlab grid world environments by defining your own size, rewards and obstacles.
create a reinforcement learning environment by supplying custom dynamic functions.
define a custom reinforcement learning environment by creating and modifying a template environment object.