38 citations · 96 across the 20 of their papers we have counts for
3 papers · 1 filter
Beyond Expected Return: Accounting for Policy Reproducibility when Evaluating Reinforcement Learning Algorithms
Manon Flageat, Bryan Lim, Antoine Cully
Many applications in Reinforcement Learning (RL) usually have noise or stochasticity present in the environment. Beyond their impact on learning, these uncertainties lead the exact…
Mix-ME: Quality-Diversity for Multi-Agent Learning
Garðar Ingvarsson, Mikayel Samvelyan, Bryan Lim +3
In many real-world systems, such as adaptive robotics, achieving a single, optimised solution may be insufficient. Instead, a diverse set of high-performing solutions is often requ…
Understanding the Synergies between Quality-Diversity and Deep Reinforcement Learning
Bryan Lim, Manon Flageat, Antoine Cully
The synergies between Quality-Diversity (QD) and Deep Reinforcement Learning (RL) have led to powerful hybrid QD-RL algorithms that have shown tremendous potential, and brings the…