Our AAMAS’21 paper on loss bounds for influence-based abstraction is online.
In this paper, we derive conditions for ‘approximate influence predictors’ to give small value-loss when used in small (abstracted) MDPs. From these conditions we conclude that that learning such AIPs with cross-entropy loss seems sensible.
Author: frans
3 Year Postdoc
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!
NeurIPS camready: MDP Homomorphic Networks
In this work we show how symmetries that can occur in MDPs can be exploited for more efficient deep reinforcement learning.
NeurIPS Camready: Multi-agent active perception with prediction rewards
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).
NeurIPS Camready: Influence-Augmented Online Planning
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.
Three papers at NeurIPS 2020
Three of our papers were accepted at NeurIPS. For short descriptions, see my tweet.
(Updated) arxiv links will follow…
Vacancy: looking for 3-year postdoc
I’m looking for a postdoc to work on learning in interactive settings. Please see https://www.fransoliehoek.net/wp/vacancies/.
AAMAS: Maximizing Information Gain via Prediction Rewards
This paper tackles the problem of active perception: taking actions to minimize one’s uncertainty. It further formalizes the link between information gain and prediction rewards, and uses this to propose a deep-learning approach to optimize active perception from a data set, thus obviating the need for a complex POMDP model.
IJCAI paper: Decentralized MCTS via Learned Teammate Models
Aleksander Czechowski got his paper on Decentralized MCTS via Learned Teammate Models accepted at IJCAI 2020.
In this paper we learn the models of other agents that each agent then uses to predict the future with. Stay tuned for the camready.
AAMAS camready: Model-based RL
Together with Thomas Kipf, Max Welling and myself, Elise van der Pol did some excellent work on model-based RL.
See:
- This post on plannable approximations with MDP homomorphisms
- The paper