10 citations · 11 across the 6 of their papers we have counts for
6 papers
Pokemon Red via Reinforcement Learning
Marco Pleines, Daniel Addis, David Rubinstein +3
Pokémon Red, a classic Game Boy JRPG, presents significant challenges as a testbed for agents, including multi-tasking, long horizons of tens of thousands of steps, hard exploratio…
Memory Gym: Towards Endless Tasks to Benchmark Memory Capabilities of Agents
Marco Pleines, Matthias Pallasch, Frank Zimmer +1
Memory Gym presents a suite of 2D partially observable environments, namely Mortar Mayhem, Mystery Path, and Searing Spotlights, designed to benchmark memory capabilities in decisi…
Improving Bidding and Playing Strategies in the Trick-Taking game Wizard using Deep Q-Networks
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). Deep Q-Ne…
On the Verge of Solving Rocket League using Deep Reinforcement Learning and Sim-to-sim Transfer
Marco Pleines, Konstantin Ramthun, Yannik Wegener +12
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 runnin…
Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization
Marco Pleines, Matthias Pallasch, Frank Zimmer +1
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 obse…
Obstacle Tower Without Human Demonstrations: How Far a Deep Feed-Forward Network Goes with Reinforcement Learning
Marco Pleines, Jenia Jitsev, Mike Preuss +1
The Obstacle Tower Challenge is the task to master a procedurally generated chain of levels that subsequently get harder to complete. Whereas the most top performing entries of las…