2 citations · 7 across the 13 of their papers we have counts for
4 papers · 1 filter
-Level Policy Gradients for Multi-Agent Reinforcement Learning
Aryaman Reddi, Gabriele Tiboni, Jan Peters +1
Actor-critic algorithms for deep multi-agent reinforcement learning (MARL) typically employ a policy update that responds to the current strategies of other agents. While being str…
Searching on a Budget: HW-NAS with 10 Latency Probes
Francesco Capuano, Gabriele Tiboni, Niccolò Cavagnero +1
Existing hardware-aware NAS (HW-NAS) methods typically assume access to precise information circa the target device, either via analytical approximations of the post-compilation la…
Shaping Laser Pulses with Reinforcement Learning
Francesco Capuano, Davorin Peceli, Gabriele Tiboni
High Power Laser (HPL) systems operate in the attoseconds regime -- the shortest timescale ever created by humanity. HPL systems are instrumental in high-energy physics, leveraging…
Domain Randomization via Entropy Maximization
Gabriele Tiboni, Pascal Klink, Jan Peters +3
Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hi…