1 code implementation • 29 Sep 2023 • Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss
Memory Gym presents a suite of 2D partially observable environments, namely Mortar Mayhem, Mystery Path, and Searing Spotlights, designed to benchmark memory capabilities in decision-making agents.
no code implementations • 27 May 2022 • Jonas Schumacher, Marco Pleines
In this work, the trick-taking game Wizard with a separate bidding and playing phase is modeled by two interleaved partially observable Markov decision processes (POMDP).
no code implementations • 23 May 2022 • Marco Pleines, Matthias Pallasch, Frank Zimmer, Mike Preuss
At first sight it may seem straightforward to use recurrent layers in Deep Reinforcement Learning algorithms to enable agents to make use of memory in the setting of partially observable environments.
no code implementations • 10 May 2022 • Marco Pleines, Konstantin Ramthun, Yannik Wegener, Hendrik Meyer, Matthias Pallasch, Sebastian Prior, Jannik Drögemüller, Leon Büttinghaus, Thilo Röthemeyer, Alexander Kaschwig, Oliver Chmurzynski, Frederik Rohkrähmer, Roman Kalkreuth, Frank Zimmer, Mike Preuss
Autonomously trained agents that are supposed to play video games reasonably well rely either on fast simulation speeds or heavy parallelization across thousands of machines running concurrently.
1 code implementation • 1 Apr 2020 • Marco Pleines, Jenia Jitsev, Mike Preuss, Frank Zimmer
The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete.