gridworldEnvironment

state

action

<p>Function defines an environment for a 2x2 gridworld example. Here an agent is intended to
navigate from an arbitrary starting position to a goal position. The grid is surrounded by a wall,
which makes it impossible for the agent to move off the grid. In addition, the agent faces a wall between s1 and s4.
If the agent reaches the goal position, it earns a reward of 10. Crossing each square of the grid results in
a negative reward of -1.</p>

Performs model-free reinforcement learning in R. This implementation enables the learning
of an optimal policy based on sample sequences consisting of states, actions and rewards. In
addition, it supplies multiple predefined reinforcement learning algorithms, such as experience
replay.

Nicolas Proellochs

ReinforcementLearning

Model-Free Reinforcement Learning

Stefan Feuerriegel

gridworldEnvironment function

Defines an environment for a gridworld example — gridworldEnvironment

Defines an environment for a gridworld example

gridworldEnvironment: Defines an environment for a gridworld example

Description

Usage

Arguments

Value