2k citations · 2.2k across the 27 of their papers we have counts for
13 papers · 1 filter
Two-Memory Reinforcement Learning
Zhao Yang, Thomas. M. Moerland, Mike Preuss +1
While deep reinforcement learning has shown important empirical success, it tends to learn relatively slow due to slow propagation of rewards information and slow update of paramet…
First Go, then Post-Explore: the Benefits 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…
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…
Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning
Matthias Müller-Brockhausen, Mike Preuss, Aske Plaat
The idea of transfer in reinforcement learning (TRL) is intriguing: being able to transfer knowledge from one problem to another problem without learning everything from scratch. T…
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, 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 have been solved in tasks such as game playing an…
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…
Analysis of Hyper-Parameters for Small Games: Iterations or Epochs in Self-Play?
Hui Wang, Michael Emmerich, Mike Preuss +1
The landmark achievements of AlphaGo Zero have created great research interest into self-play in reinforcement learning. In self-play, Monte Carlo Tree Search is used to train a de…
Hyper-Parameter Sweep on AlphaZero General
Hui Wang, Michael Emmerich, Mike Preuss +1
Since AlphaGo and AlphaGo Zero have achieved breakground successes in the game of Go, the programs have been generalized to solve other tasks. Subsequently, AlphaZero was developed…