180 citations · 601 across the 46 of their papers we have counts for
6 papers · 1 filter
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning
Xueguang Lyu, Christopher Amato
When multiple agents learn in a decentralized manner, the environment appears non-stationary from the perspective of an individual agent due to the exploration and learning of the…
Bayesian Reinforcement Learning in Factored POMDPs
Sammie Katt, Frans Oliehoek, Christopher Amato
Bayesian approaches provide a principled solution to the exploration-exploitation trade-off in Reinforcement Learning. Typical approaches, however, either assume a fully observable…
Reconciling -Returns with Experience Replay
Brett Daley, Christopher Amato
Modern deep reinforcement learning methods have departed from the incremental learning required for eligibility traces, rendering the implementation of the -return difficult in…
The Art of Drafting: A Team-Oriented Hero Recommendation System for Multiplayer Online Battle Arena Games
Zhengxing Chen, Truong-Huy D Nguyen, Yuyu Xu +4
Multiplayer Online Battle Arena (MOBA) games have received increasing popularity recently. In a match of such games, players compete in two teams of five, each controlling an in-ga…
Learning in POMDPs with Monte Carlo Tree Search
Sammie Katt, Frans A. Oliehoek, Christopher Amato
The POMDP is a powerful framework for reasoning under outcome and information uncertainty, but constructing an accurate POMDP model is difficult. Bayes-Adaptive Partially Observabl…
Learning to Teach in Cooperative Multiagent Reinforcement Learning
Shayegan Omidshafiei, Dong-Ki Kim, Miao Liu +5
Collective human knowledge has clearly benefited from the fact that innovations by individuals are taught to others through communication. Similar to human social groups, agents in…