Environment: Custom Reinforcement Learning Environment

In reinforcelearn: Reinforcement Learning

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)

reinforcelearn documentation built on May 2, 2019, 9:20 a.m.