Search Results for author: Guy Avni

Found 7 papers, 0 papers with code

All-Pay Bidding Games on Graphs

no code implementations19 Nov 2019 Guy Avni, Rasmus Ibsen-Jensen, Josef Tkadlec

On the positive side, we show a simple FPTAS for DAGs, that, for each budget ratio, outputs an approximation for the optimal strategy for that ratio.

Open-Ended Question Answering

Formal Methods with a Touch of Magic

no code implementations25 May 2020 Parand Alizadeh Alamdari, Guy Avni, Thomas A. Henzinger, Anna Lukina

Machine learning and formal methods have complimentary benefits and drawbacks.

Infinite-Duration All-Pay Bidding Games

no code implementations12 May 2020 Guy Avni, Ismaël Jecker, Đorđe Žikelić

In {\em bidding games}, however, the players have budgets, and in each turn, we hold an "auction" (bidding) to determine which player moves the token: both players simultaneously submit bids and the higher bidder moves the token.

ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents

no code implementations24 Jan 2023 Yotam Amitai, Guy Avni, Ofra Amir

As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial.

reinforcement-learning Reinforcement Learning (RL)

Reachability Poorman Discrete-Bidding Games

no code implementations27 Jul 2023 Guy Avni, Tobias Meggendorfer, Suman Sadhukhan, Josef Tkadlec, Đorđe Žikelić

We consider, for the first time, {\em poorman discrete-bidding} in which the granularity of the bids is restricted and the higher bid is paid to the bank.

Auction-Based Scheduling

no code implementations18 Oct 2023 Guy Avni, Kaushik Mallik, Suman Sadhukhan

Policies express their scheduling urgency using their bids and the bounded budgets ensure long-run scheduling fairness.

Decision Making Fairness +1

Synthesis of Hierarchical Controllers Based on Deep Reinforcement Learning Policies

no code implementations21 Feb 2024 Florent Delgrange, Guy Avni, Anna Lukina, Christian Schilling, Ann Nowé, Guillermo A. Pérez

We propose a novel approach to the problem of controller design for environments modeled as Markov decision processes (MDPs).

reinforcement-learning

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