Nothing
## ----setup, include=FALSE------------------------------------------------
knitr::opts_chunk$set(message = TRUE, eval = TRUE, collapse = TRUE, comment = "#>")
## ------------------------------------------------------------------------
library(reinforcelearn)
## ---- out.width = "200px", fig.align="center", echo = FALSE--------------
knitr::include_graphics("mountaincar.JPG")
## ------------------------------------------------------------------------
reset = function(self) {
position = runif(1, -0.6, -0.4)
velocity = 0
state = matrix(c(position, velocity), ncol = 2)
state
}
## ------------------------------------------------------------------------
step = function(self, action) {
position = self$state[1]
velocity = self$state[2]
velocity = (action - 1L) * 0.001 + cos(3 * position) * (-0.0025)
velocity = min(max(velocity, -0.07), 0.07)
position = position + velocity
if (position < -1.2) {
position = -1.2
velocity = 0
}
state = matrix(c(position, velocity), ncol = 2)
reward = -1
if (position >= 0.5) {
done = TRUE
reward = 0
} else {
done = FALSE
}
list(state, reward, done)
}
## ------------------------------------------------------------------------
env = makeEnvironment(step = step, reset = reset)
## ---- eval = FALSE-------------------------------------------------------
# # Create a gym environment.
# env = makeEnvironment("gym", gym.name = "MountainCar-v0")
## ------------------------------------------------------------------------
# State transition array
P = array(0, c(2, 2, 2))
P[, , 1] = matrix(c(0.5, 0.5, 0, 1), 2, 2, byrow = TRUE)
P[, , 2] = matrix(c(0.1, 0.9, 0, 1), 2, 2, byrow = TRUE)
# Reward matrix
R = matrix(c(5, 10, -1, 2), 2, 2, byrow = TRUE)
env = makeEnvironment("mdp", transitions = P, rewards = R)
## ---- out.width = "200px", fig.align="center", echo = FALSE--------------
knitr::include_graphics("gridworld.JPG")
## ------------------------------------------------------------------------
# Gridworld Environment (Sutton & Barto (2017) Example 4.1)
env = makeEnvironment("gridworld", shape = c(4, 4), goal.states = c(0, 15))
## ------------------------------------------------------------------------
env = makeEnvironment("gridworld", shape = c(4, 4),
goal.states = 0L, initial.state = 15L)
## ------------------------------------------------------------------------
# The initial state of the environment.
env$reset()
env$visualize()
# Actions are encoded as integers.
env$step(0L)
env$visualize()
# But can also have character names.
env$step("left")
env$visualize()
## ------------------------------------------------------------------------
env = makeEnvironment("mountain.car")
env$n.actions
env$state.space.bounds
## ------------------------------------------------------------------------
env = makeEnvironment("gridworld", shape = c(4, 4),
goal.states = 0L, initial.state = 15L, discount = 0.99)
env$step("up")
env$n.step
env$episode.return
env$step("left")
env$n.step
env$episode.return
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