111 citations · 326 across the 9 of their papers we have counts for
5 papers · 1 filter
Robust Visual Domain Randomization for Reinforcement Learning
Reda Bahi Slaoui, William R. Clements, Jakob N. Foerster +1
Producing agents that can generalize to a wide range of visually different environments is a significant challenge in reinforcement learning. One method for overcoming this issue i…
Exploratory Combinatorial Optimization with Reinforcement Learning
Thomas D. Barrett, William R. Clements, Jakob N. Foerster +1
Many real-world problems can be reduced to combinatorial optimization on a graph, where the subset or ordering of vertices that maximize some objective function must be found. With…
Tuning between photon-number and quadrature measurements with weak-field homodyne detection
G. S. Thekkadath, D. S. Phillips, J. F. F. Bulmer +10
Variable measurement operators enable the optimization of strategies for testing quantum properties and the preparation of a range of quantum states. Here, we experimentally implem…
Quantum simulations with multiphoton Fock states
T. Sturges, T. McDermott, A. Buraczewski +9
Quantum simulations are becoming an essential tool for studying complex phenomena, e.g. quantum topology, quantum information transfer, and relativistic wave equations, beyond the…
Estimating Risk and Uncertainty in Deep Reinforcement Learning
William R. Clements, Bastien Van Delft, Benoît-Marie Robaglia +2
Reinforcement learning agents are faced with two types of uncertainty. Epistemic uncertainty stems from limited data and is useful for exploration, whereas aleatoric uncertainty ar…