Do you have experience in multiagent reinforcement learning, game theory and/or other forms of interactive learning? Then have a look at this vacancy and contact me!
In this work we show how symmetries that can occur in MDPs can be exploited for more efficient deep reinforcement learning.
This paper shows that also in decentralized multiagent settings we can employ “prediction rewards” for active perception. (Intuitively leading to a type of voting that we try to optimize).
The camready version of Influence-Augmented Online Planning for Complex Environments is now available.
In this work, we show that by learning approximate representations of influence, we can speed up online planning (POMCP) sufficiently to get better performance when the time for online decision making is constrained.