The authors extend the Almgren-Chriss model to a multi-agent setting and apply deep reinforcement learning to simulate and optimize liquidation strategies under practical constraints.
Deep reinforcement learning with double q-learning
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
A hierarchical DRL architecture generates lane-change commands from occupancy grids for stochastic highway driving and claims improved reliability over end-to-end methods.
citing papers explorer
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Multi-Agent Deep Reinforcement Learning for Liquidation Strategy Analysis
The authors extend the Almgren-Chriss model to a multi-agent setting and apply deep reinforcement learning to simulate and optimize liquidation strategies under practical constraints.
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A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning
A hierarchical DRL architecture generates lane-change commands from occupancy grids for stochastic highway driving and claims improved reliability over end-to-end methods.