Jurisprudence It Was One of DOGE's Most Absurd Abuses. One year ago, the Trump administration canceled more than 1,400 grants from the National Endowment for the Humanities. More than $100 million in congressionally appropriated funds awarded to scholars, writers, archivists, and researchers across the country was snatched up in three days. There was no due process. Just a chatbot and two guys from DOGE who had no legal authority to be there in the first place.

A.1 Algorithm The code is available at https://github.com/mklissa/MOC. A.2 Tabular experiments A.2.1 Implementation Details For our experiments of the FourRooms domain we based our implementation on [Bacon et al., 2016] and ran the experiments for 500 episodes that last a maximum of 1000 steps with goal located in the right hallway. In the first experiment we verify whether learning a fixed set of options can be accelerated by our method. We define this fixed set as the hallway options from Sutton et al. [1999b]. As the policies of these options were deterministic and we use importance sampling, we relax them to stochastic policies where the most likely action is the one leading to a hallway.

Synchronizing decisions across multiple agents in realistic settings is problematic since it requires agents to wait for other agents to terminate and communicate about termination reliably. Ideally, agents should learn and execute asynchronously instead. Such asynchronous methods also allow temporally extended actions that can take different amounts of time based on the situation and action executed. Unfortunately, current policy gradient methods are not applicable in asynchronous settings, as they assume that agents synchronously reason about action selection at every time step. To allow asynchronous learning and decision-making, we formulate a set of asynchronous multi-agent actor-critic methods that allow agents to directly optimize asynchronous policies in three standard training paradigms: decentralized learning, centralized learning, and centralized training for decentralized execution. Empirical results (in simulation and hardware) in a variety of realistic domains demonstrate the superiority of our approaches in large multi-agent problems and validate the effectiveness of our algorithms for learning high-quality and asynchronous solutions.

Temporally-extended actions have been widely used in both learning and planning to improve scalability and reduce complexity.

A matching process on a two-sided market can determine who gets which job [40, 31], school place[44],orspouse.

Temporal abstractions in the form of options have been shown to help reinforcement learning (RL) agents learn faster.