re: UKPol BBC election debate show
30 second freestyle round:
Lab: No more chaos, time for change. We already changed. Stability, NHS, and border-cops and teachers.
Scots: We will put scotland first. Action on NHS, rejoin single market, get green growth and don't collude with tories like Labour.
Greens: Labour offer more conservatives. We can break through, and will defend the future. no private NHS, vote green for hope.
Wales: Send Labour the message to stop taking wales for granted.
Con: It's been tough, labour will tax you and our plan is working, we'll cut taxes and keep pensions.
Lib: Crying out for change! Everything is broken. We'll fix the NHS and social care and get the shit out of the rivers.
Reform: Politics isn't working, the two parties are the same. We can be real opposition against Labour. Join the revolt.
Everyone thinks the conservatives are finished, and mentioned it.
#ukpol #election #debate
Tree Search for Simultaneous Move Games via Equilibrium Approximation
Ryan Yu, Alex Olshevsky, Peter Chin
https://arxiv.org/abs/2406.10411 https://arxiv.org/pdf/2406.10411
arXiv:2406.10411v1 Announce Type: new
Abstract: Neural network supported tree-search has shown strong results in a variety of perfect information multi-agent tasks. However, the performance of these methods on partial information games has generally been below competing approaches. Here we study the class of simultaneous-move games, which are a subclass of partial information games which are most similar to perfect information games: both agents know the game state with the exception of the opponent's move, which is revealed only after each agent makes its own move. Simultaneous move games include popular benchmarks such as Google Research Football and Starcraft.
In this study we answer the question: can we take tree search algorithms trained through self-play from perfect information settings and adapt them to simultaneous move games without significant loss of performance? We answer this question by deriving a practical method that attempts to approximate a coarse correlated equilibrium as a subroutine within a tree search. Our algorithm works on cooperative, competitive, and mixed tasks. Our results are better than the current best MARL algorithms on a wide range of accepted baseline environments.
QCD in the chiral SU(3) limit from baryon masses on Lattice QCD ensembles
Matthias F. M. Lutz, Yonggoo Heo, Renwick J. Hudspith
https://arxiv.org/abs/2406.07442