In a two-agent Almgren-Chriss liquidation game, deep RL agents given intra-episode history of prices and own actions achieve supra-competitive outcomes more frequently and persistently than agents without such memory.
The RAND Journal of Economics , volume=
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In untrusted strategic settings with unreliable measurements, the quantum primitive for output-hiding function sharing permits parties to generate private unbiased coins.
Proposes a quantum primitive for output-hiding function sharing with applications to enhanced QKD security and hidden joint function encoding.
citing papers explorer
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Memory-Induced Supra-Competitive Outcomes Between Deep Reinforcement Learning Agents in Optimal Trade Execution
In a two-agent Almgren-Chriss liquidation game, deep RL agents given intra-episode history of prices and own actions achieve supra-competitive outcomes more frequently and persistently than agents without such memory.
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Quantum Primitive for Output-Hiding Function Sharing: Strategic Settings
In untrusted strategic settings with unreliable measurements, the quantum primitive for output-hiding function sharing permits parties to generate private unbiased coins.
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Quantum Primitive for Output-Hiding Function Sharing: QKD and Joint Computation Applications
Proposes a quantum primitive for output-hiding function sharing with applications to enhanced QKD security and hidden joint function encoding.