
Reinforcement Learning for Datacenter Congestion Control
We approach the task of network congestion control in datacenters using ...
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Acting in Delayed Environments with NonStationary Markov Policies
The standard Markov Decision Process (MDP) formulation hinges on the ass...
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The Architectural Implications of Distributed Reinforcement Learning on CPUGPU Systems
With deep reinforcement learning (RL) methods achieving results that exc...
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A Tale of TwoTimescale Reinforcement Learning with the Tightest FiniteTime Bound
Policy evaluation in reinforcement learning is often conducted using two...
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How to Combine TreeSearch Methods in Reinforcement Learning
Finitehorizon lookahead policies are abundantly used in Reinforcement L...
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MultipleStep Greedy Policies in Online and Approximate Reinforcement Learning
Multiplestep lookahead policies have demonstrated high empirical compet...
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Beyond the One Step Greedy Approach in Reinforcement Learning
The famous Policy Iteration algorithm alternates between policy improvem...
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Safe Exploration in Continuous Action Spaces
We address the problem of deploying a reinforcement learning (RL) agent ...
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ChanceConstrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to ...
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Finite Sample Analyses for TD(0) with Function Approximation
TD(0) is one of the most commonly used algorithms in reinforcement learn...
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Unit Commitment using Nearest Neighbor as a ShortTerm Proxy
We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be us...
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Hierarchical Decision Making In Electricity Grid Management
The power grid is a complex and vital system that necessitates careful r...
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Gal Dalal
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