RLTest

RLTest is a C++ library for testing reinforcement learning algorithms. It was
developed in parallel to a course in reinforcement learning in 2016.

The main goal was to implement a couple of reinforcement learning algorithms
and a couple of artificial worlds in which the actions of the algorithms can be
evaluated and compared.

Requirements:

• C++ compiler
• CMake

Installation

git clone https://github.com/mo42/RLEval.git && cd RLEval
git submodule update --init --recursive
mkdir build && cd build
cmake ../
make

Algorithms

• PoWER
• Simple Policy Gradient
• SARSA (discrete)
• Q-learning (discrete)
• TDLearning (discrete)

Worlds

• Cart pole world (balancing a pole on a cart)
• Mountain car world (drive car uphill by building up momentum)
• Simple and discrete maze world
• Discrete cliff world (
• Adapter world (a continuous world that can be instantiated with a discrete
  world. With one-hot coding, algorithms for continuous worlds can work on
  discrete worlds.)