Just a short update for the people who applied to our open positions in the MMLL. Due to a combination of holidays, new recruitment system, and a large number of applications, we are still screening. We hope to have more news soon!
As one of the scientific directors, I am co-leading the new Mercury Machine Learning Lab: a new ICAI lab in collaboration with the University of Amsterdam and booking.com.
At Delft, we will be looking for 2 PhDs and a postdoc, so keep an eye out on adverts or follow me on twiter if interested in applying reinforcement learning in a real world context!
Jacopo, Rahul, Sam and I won the best paper award at ALA’21!
-> check out the paper here.
Tomorrow, wed 5th of May, I will lead an informal discussion on multiagent RL. Details can be found here: https://aamas2021.soton.ac.uk/programme/detailed-programme/#Wednesday-M-INF
Looking forward to discuss!
At next AAMAS, Jacopo Castellini, Sam Devlin, Rahul Savani and myself, will present our work on combining difference rewards and policy gradient methods.
Main idea: for differencing the function needs to be quite accurate. As such doing differencing on Q-functions (as COMA) might not be ideal. We instead perform the differencing on the reward function, which may be known and otherwise easier to learn (stationary). Our results show potential for great improvements especially for larger number of agents.
That is what we explore in our AAMAS’21 blue sky paper.
The idea is to explicitly model non-stationarity as part of an environmental shift game (ESG). This enables us to predict and even steer the shifts that would occur, while dealing with epistemic uncertainty in a robust manner.
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.
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.