research
∙
01/30/2023
SoftTreeMax: Exponential Variance Reduction in Policy Gradient via Tree Search
Despite the popularity of policy gradient methods, they are known to suf...
share
research
∙
09/28/2022
SoftTreeMax: Policy Gradient with Tree Search
Policy-gradient methods are widely used for learning control policies. T...
research
∙
07/05/2022
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
Cloud datacenters are exponentially growing both in numbers and size. Th...
research
∙
05/30/2022
Reinforcement Learning with a Terminator
We present the problem of reinforcement learning with exogenous terminat...
research
∙
01/28/2022
Planning and Learning with Adaptive Lookahead
The classical Policy Iteration (PI) algorithm alternates between greedy ...
research
∙
10/13/2021
On Covariate Shift of Latent Confounders in Imitation and Reinforcement Learning
We consider the problem of using expert data with unobserved confounders...
research
∙
07/04/2021
Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction
Tree Search (TS) is crucial to some of the most influential successes in...
research
∙
02/18/2021
Reinforcement Learning for Datacenter Congestion Control
We approach the task of network congestion control in datacenters using ...
research
∙
01/28/2021
Acting in Delayed Environments with Non-Stationary Markov Policies
The standard Markov Decision Process (MDP) formulation hinges on the ass...
research
∙
12/08/2020
The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems
With deep reinforcement learning (RL) methods achieving results that exc...
research
∙
11/20/2019
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound
Policy evaluation in reinforcement learning is often conducted using two...
research
∙
09/06/2018
How to Combine Tree-Search Methods in Reinforcement Learning
Finite-horizon lookahead policies are abundantly used in Reinforcement L...
research
∙
05/21/2018
Multiple-Step Greedy Policies in Online and Approximate Reinforcement Learning
Multiple-step lookahead policies have demonstrated high empirical compet...
research
∙
02/10/2018
Beyond the One Step Greedy Approach in Reinforcement Learning
The famous Policy Iteration algorithm alternates between policy improvem...
research
∙
01/26/2018
Safe Exploration in Continuous Action Spaces
We address the problem of deploying a reinforcement learning (RL) agent ...
research
∙
01/01/2018
Chance-Constrained Outage Scheduling using a Machine Learning Proxy
Outage scheduling aims at defining, over a horizon of several months to ...
research
∙
04/04/2017
Finite Sample Analyses for TD(0) with Function Approximation
TD(0) is one of the most commonly used algorithms in reinforcement learn...
research
∙
11/30/2016
Unit Commitment using Nearest Neighbor as a Short-Term Proxy
We devise the Unit Commitment Nearest Neighbor (UCNN) algorithm to be us...
research
∙
03/06/2016
