Safe Multi-agent Learning via Trapping Regions

no code implementations • 27 Feb 2023 • Aleksander Czechowski, Frans A. Oliehoek

One of the main challenges of multi-agent learning lies in establishing convergence of the algorithms, as, in general, a collection of individual, self-serving agents is not guaranteed to converge with their joint policy, when learning concurrently.

Generative Adversarial Network

RangL: A Reinforcement Learning Competition Platform

no code implementations • 28 Jul 2022 • Viktor Zobernig, Richard A. Saldanha, Jinke He, Erica van der Sar, Jasper van Doorn, Jia-Chen Hua, Lachlan R. Mason, Aleksander Czechowski, Drago Indjic, Tomasz Kosmala, Alessandro Zocca, Sandjai Bhulai, Jorge Montalvo Arvizu, Claude Klöckl, John Moriarty

The RangL project hosted by The Alan Turing Institute aims to encourage the wider uptake of reinforcement learning by supporting competitions relating to real-world dynamic decision problems.

OpenAI Gym reinforcement-learning +1

Decentralized MCTS via Learned Teammate Models

no code implementations • 19 Mar 2020 • Aleksander Czechowski, Frans A. Oliehoek

Decentralized online planning can be an attractive paradigm for cooperative multi-agent systems, due to improved scalability and robustness.

Influence-aware Memory Architectures for Deep Reinforcement Learning

1 code implementation • 18 Nov 2019 • Miguel Suau, Jinke He, Elena Congeduti, Rolf A. N. Starre, Aleksander Czechowski, Frans A. Oliehoek

Due to its perceptual limitations, an agent may have too little information about the state of the environment to act optimally.

reinforcement-learning Reinforcement Learning (RL)