Computational Resources

Deterministic Planning

• http://planning.domains
  A collection of tools for working with planning domains, including access to a large suite of benchmark domains from over a decade of IPC (International Planning Competition) domains, APIs for running different planner code, and an editor for creating new domains in PDDL, the standard input format for (nonprobabilistic) planning problems.
• https://www.ida.liu.se/~TDDD48/labs/2016/planners.en.shtml
  A great web page with an overview of different planners and instructions on how to run them, plus much more, by Jonas Kvarnstrom from Linkopings University.

Pathfinding Benchmarks

• http://movingai.com/benchmarks
  A collection of benchmark problems for assessing pathfinding algorithms. These benchmarks include 2-D maps and mazes from video games as well as randomly generated maps.

Frameworks for Heuristic Search

• PSVN: Please email profs for access.
  A language for defining search problems, a compiler from the language to C code, and search code to build pattern databases and run standard algorithms.
• HOG2: https://github.com/nathansttt/hog2
  A search framework with a variety of algorithms and domains. The assignment code bases some of its code on HOG2.
• SBPL: http://www.sbpl.net
  Code for search for robotics.

Motion Planning

• http://ompl.kavrakilab.org
  
  Open Motion Planning Library (OMPL)

Reinforcement Learning

• https://github.com/aikorea/awesome-rl
  This "awesome" page has all sorts of RL resources -- code, papers, applications.
• http://www.wildml.com/2016/10/learning-reinforcement-learning/
  This page is an up-to-date learning environment for RL that is hooked up to code and exercises in OpenAI Gym.
• https://gym.openai.com/
  Here is the direct link to OpenAI Gym, a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents everything from walking to playing games like Pong or Go.

Arcade Learning Environment (ALE)

• http://www.arcadelearningenvironment.org/
  The Arcade Learning Environment (ALE) is a simple object-oriented framework that allows researchers and hobbyists to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of emulation from agent design.

Deep Reinforcement Learning

• https://github.com/muupan/deep-reinforcement-learning-papers
  Here is a nice collection of papers, talk slides, etc. on Deep RL.
• https://github.com/nivwusquorum/tensorflow-deepq
  GitHub -- Code for RL using tensor flow
• http://videolectures.net/rldm2015_silver_reinforcement_learning/
  Tutorial by David Silver on Deep RL.

More Games Environments

• Mario: web -- http://www.marioai.org/
  paper -- http://julian.togelius.com/Karakovskiy2012The.pdf
  This API for playing Mario provides arrays that describe whether terrain is walkable and whether there are enemies. The paper describes different approaches (learning based and hand-crafted) that entered a Mario AI competition.
• Minecraft: https://www.microsoft.com/en-us/research/project/project-malmo/
  This is a very popular game nowadays and Microsoft released Project Malmo: a challenge to create AI agents using the Minecraft environment. It is interesting, as the API has access to discrete actions, grid mode (i.e. full observability) and also screen mode (i.e. video rendering). RL people may say the grid mode is "cheating", though.