Environment: Custom Reinforcement Learning Environment
In markdumke/reinforceR: Reinforcement Learning

Environment

R Documentation

Custom Reinforcement Learning Environment

Description

Custom Reinforcement Learning Environment

Arguments

`step`	[`function(self, action)`] Custom step function.
`reset`	[`function(self)`] Custom reset function.
`visualize`	[`function(self)`] Optional custom visualization function.
`discount`	[`numeric(1)` in (0, 1)] Discount factor.
`action.names`	[`named integer`] Optional action names for a discrete action space.

Usage

makeEnvironment("custom", step, reset, visualize = NULL, discount = 1, action.names = NULL)

Methods

$step(action)
Take action in environment. Returns a list with state, reward, done.
$reset()
Resets the done flag of the environment and returns an initial state. Useful when starting a new episode.
$visualize()
Visualizes the environment (if there is a visualization function).

Examples

step = function(self, action) {
  state = list(mean = action + rnorm(1), sd = runif(1))
  reward = rnorm(1, state[[1]], state[[2]])
  done = FALSE
  list(state, reward, done)
}

reset = function(self) {
  state = list(mean = 0, sd = 1)
  state
}

env = makeEnvironment(step = step, reset = reset)
env$reset()
env$step(100)

markdumke/reinforceR documentation built on Nov. 17, 2022, 12:53 a.m.