152 citations · 247 across the 5 of their papers we have counts for
5 papers
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization
Philipp Dahlinger, Philipp Becker, Maximilian Hüttenrauch +1
Stochastic gradient-based optimization is crucial to optimize neural networks. While popular approaches heuristically adapt the step size and direction by rescaling gradients, a mo…
Regret-Aware Black-Box Optimization with Natural Gradients, Trust-Regions and Entropy Control
Maximilian Hüttenrauch, Gerhard Neumann
Most successful stochastic black-box optimizers, such as CMA-ES, use rankings of the individual samples to obtain a new search distribution. Yet, the use of rankings also introduce…
Deep Reinforcement Learning for Swarm Systems
Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to repr…
Guided Deep Reinforcement Learning for Swarm Systems
Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
In this paper, we investigate how to learn to control a group of cooperative agents with limited sensing capabilities such as robot swarms. The agents have only very basic sensor c…
Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning
Maximilian Hüttenrauch, Adrian Šošić, Gerhard Neumann
Swarm systems constitute a challenging problem for reinforcement learning (RL) as the algorithm needs to learn decentralized control policies that can cope with limited local sensi…