13 citations · 23 across the 7 of their papers we have counts for
10 papers
Safe Multi-agent Learning via Trapping Regions
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 guaran…
RangL: A Reinforcement Learning Competition Platform
Viktor Zobernig, Richard A. Saldanha, Jinke He +12
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 decisio…
Poincaré-Bendixson Limit Sets in Multi-Agent Learning
Aleksander Czechowski, Georgios Piliouras
A key challenge of evolutionary game theory and multi-agent learning is to characterize the limit behavior of game dynamics. Whereas convergence is often a property of learning alg…
Decentralized MCTS via Learned Teammate Models
Aleksander Czechowski, Frans A. Oliehoek
Decentralized online planning can be an attractive paradigm for cooperative multi-agent systems, due to improved scalability and robustness. A key difficulty of such approach lies…
Influence-aware Memory Architectures for Deep Reinforcement Learning
Miguel Suau, Jinke He, Elena Congeduti +3
Due to its perceptual limitations, an agent may have too little information about the state of the environment to act optimally. In such cases, it is important to keep track of the…
Rigorous numerics for a singular perturbation problem
Aleksander Czechowski
Fast-slow systems are notoriously difficult to analyze with rigorous numerics, since the qualitative properties of the solution space change fundamentally when the so-called small…