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Found 7 papers, 0 papers with code
Synthesis of Hierarchical Controllers Based on Deep Reinforcement Learning Policies
We propose a novel approach to the problem of controller design for environments modeled as Markov decision processes (MDPs).
Auction-Based Scheduling
Policies express their scheduling urgency using their bids and the bounded budgets ensure long-run scheduling fairness.
Reachability Poorman Discrete-Bidding Games
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.
ASQ-IT: Interactive Explanations for Reinforcement-Learning Agents
As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial.
Formal Methods with a Touch of Magic
Machine learning and formal methods have complimentary benefits and drawbacks.
Infinite-Duration All-Pay Bidding Games
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.
All-Pay Bidding Games on Graphs
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.
