58 citations · 100 across the 12 of their papers we have counts for
10 papers · 1 filter
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…
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation
Zhao Yang, Thomas M. Moerland, Mike Preuss +1
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards. The key insight of Go-Explore was that successful exploration req…
Reliable validation of Reinforcement Learning Benchmarks
Matthias Müller-Brockhausen, Aske Plaat, Mike Preuss
Reinforcement Learning (RL) is one of the most dynamic research areas in Game AI and AI as a whole, and a wide variety of games are used as its prominent test problems. However, it…
Potential-based Reward Shaping in Sokoban
Zhao Yang, Mike Preuss, Aske Plaat
Learning to solve sparse-reward reinforcement learning problems is difficult, due to the lack of guidance towards the goal. But in some problems, prior knowledge can be used to aug…
High-Accuracy Model-Based Reinforcement Learning, a Survey
Aske Plaat, Walter Kosters, Mike Preuss
Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems from game playing and robotics have been solved w…