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A Comparative Analysis of Deep Reinforcement Learning-enabled Freeway Decision-making for Automated Vehicles
Deep reinforcement learning (DRL) is becoming a prevalent and powerful m...
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Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning
Deep reinforcement learning (DRL) has reached super human levels in comp...
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Simulated Autonomous Driving on Realistic Road Networks using Deep Reinforcement Learning
Using Deep Reinforcement Learning (DRL) can be a promising approach to h...
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Decision-making for Autonomous Vehicles on Highway: Deep Reinforcement Learning with Continuous Action Horizon
Decision-making strategy for autonomous vehicles de-scribes a sequence o...
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Diverse Behavior Is What Game AI Needs: Generating Varied Human-Like Playing Styles Using Evolutionary Multi-Objective Deep Reinforcement Learning
Designing artificial intelligence for games (Game AI) has been long reco...
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Scaling up budgeted reinforcement learning
Can we learn a control policy able to adapt its behaviour in real time s...
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GST: Group-Sparse Training for Accelerating Deep Reinforcement Learning
Deep reinforcement learning (DRL) has shown remarkable success in sequen...
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Bridging the gap between Markowitz planning and deep reinforcement learning
While researchers in the asset management industry have mostly focused on techniques based on financial and risk planning techniques like Markowitz efficient frontier, minimum variance, maximum diversification or equal risk parity, in parallel, another community in machine learning has started working on reinforcement learning and more particularly deep reinforcement learning to solve other decision making problems for challenging task like autonomous driving, robot learning, and on a more conceptual side games solving like Go. This paper aims to bridge the gap between these two approaches by showing Deep Reinforcement Learning (DRL) techniques can shed new lights on portfolio allocation thanks to a more general optimization setting that casts portfolio allocation as an optimal control problem that is not just a one-step optimization, but rather a continuous control optimization with a delayed reward. The advantages are numerous: (i) DRL maps directly market conditions to actions by design and hence should adapt to changing environment, (ii) DRL does not rely on any traditional financial risk assumptions like that risk is represented by variance, (iii) DRL can incorporate additional data and be a multi inputs method as opposed to more traditional optimization methods. We present on an experiment some encouraging results using convolution networks.
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