6 citations · 22 across the 14 of their papers we have counts for
5 papers · 1 filter
Challenges for Reinforcement Learning in Quantum Circuit Design
Philipp Altmann, Jonas Stein, Michael Kölle +5
Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and adv…
Multi-Agent Quantum Reinforcement Learning using Evolutionary Optimization
Michael Kölle, Felix Topp, Thomy Phan +3
Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new ap…
CROP: Towards Distributional-Shift Robust Reinforcement Learning using Compact Reshaped Observation Processing
Philipp Altmann, Fabian Ritz, Leonard Feuchtinger +3
The safe application of reinforcement learning (RL) requires generalization from limited training data to unseen scenarios. Yet, fulfilling tasks under changing circumstances is a…
DIRECT: Learning from Sparse and Shifting Rewards using Discriminative Reward Co-Training
Philipp Altmann, Thomy Phan, Fabian Ritz +2
We propose discriminative reward co-training (DIRECT) as an extension to deep reinforcement learning algorithms. Building upon the concept of self-imitation learning (SIL), we intr…
Attention-Based Recurrence for Multi-Agent Reinforcement Learning under Stochastic Partial Observability
Thomy Phan, Fabian Ritz, Philipp Altmann +5
Stochastic partial observability poses a major challenge for decentralized coordination in multi-agent reinforcement learning but is largely neglected in state-of-the-art research…