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09/03/2023
Solving Non-Rectangular Reward-Robust MDPs via Frequency Regularization
In robust Markov decision processes (RMDPs), it is assumed that the rewa...
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07/25/2023
Implicitly Normalized Explicitly Regularized Density Estimation
We propose a new approach to non-parametric density estimation, that is ...
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06/24/2023
Individualized Dosing Dynamics via Neural Eigen Decomposition
Dosing models often use differential equations to model biological dynam...
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06/09/2023
Robust Reinforcement Learning via Adversarial Kernel Approximation
Robust Markov Decision Processes (RMDPs) provide a framework for sequent...
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05/31/2023
Representation-Driven Reinforcement Learning
We present a representation-driven framework for reinforcement learning....
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05/02/2023
CALM: Conditional Adversarial Latent Models for Directable Virtual Characters
In this work, we present Conditional Adversarial Latent Models (CALM), a...
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03/12/2023
Twice Regularized Markov Decision Processes: The Equivalence between Robustness and Regularization
Robust Markov decision processes (MDPs) aim to handle changing or partia...
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01/31/2023
An Efficient Solution to s-Rectangular Robust Markov Decision Processes
We present an efficient robust value iteration for -rectangular robust M...
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01/31/2023
Policy Gradient for s-Rectangular Robust Markov Decision Processes
We present a novel robust policy gradient method (RPG) for s-rectangular...
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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...
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01/26/2023
Train Hard, Fight Easy: Robust Meta Reinforcement Learning
A major challenge of reinforcement learning (RL) in real-world applicati...
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01/03/2023
Towards Deployable RL – What's Broken with RL Research and a Potential Fix
Reinforcement learning (RL) has demonstrated great potential, but is cur...
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12/13/2022
DiffStack: A Differentiable and Modular Control Stack for Autonomous Vehicles
Autonomous vehicle (AV) stacks are typically built in a modular fashion,...
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10/05/2022
Tractable Optimality in Episodic Latent MABs
We consider a multi-armed bandit problem with M latent contexts, where a...
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10/05/2022
Reward-Mixing MDPs with a Few Latent Contexts are Learnable
We consider episodic reinforcement learning in reward-mixing Markov deci...
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10/03/2022
Policy Gradient for Reinforcement Learning with General Utilities
In Reinforcement Learning (RL), the goal of agents is to discover an opt...
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09/28/2022
SoftTreeMax: Policy Gradient with Tree Search
Policy-gradient methods are widely used for learning control policies. T...
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07/19/2022
Actor-Critic based Improper Reinforcement Learning
We consider an improper reinforcement learning setting where a learner i...
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07/05/2022
Implementing Reinforcement Learning Datacenter Congestion Control in NVIDIA NICs
Cloud datacenters are exponentially growing both in numbers and size. Th...
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06/26/2022
Analysis of Stochastic Processes through Replay Buffers
Replay buffers are a key component in many reinforcement learning scheme...
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05/30/2022
Reinforcement Learning with a Terminator
We present the problem of reinforcement learning with exogenous terminat...
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05/28/2022
Efficient Policy Iteration for Robust Markov Decision Processes via Regularization
Robust Markov decision processes (MDPs) provide a general framework to m...
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05/10/2022
Efficient Risk-Averse Reinforcement Learning
In risk-averse reinforcement learning (RL), the goal is to optimize some...
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04/18/2022
Optimizing Tensor Network Contraction Using Reinforcement Learning
Quantum Computing (QC) stands to revolutionize computing, but is current...
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03/12/2022
Whats Missing? Learning Hidden Markov Models When the Locations of Missing Observations are Unknown
The Hidden Markov Model (HMM) is one of the most widely used statistical...
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02/02/2022
Learning to reason about and to act on physical cascading events
Reasoning and interacting with dynamic environments is a fundamental pro...
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01/31/2022
Continuous Forecasting via Neural Eigen Decomposition of Stochastic Dynamics
Motivated by a real-world problem of blood coagulation control in Hepari...
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01/30/2022
The Geometry of Robust Value Functions
The space of value functions is a fundamental concept in reinforcement l...
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01/30/2022
Coordinated Attacks against Contextual Bandits: Fundamental Limits and Defense Mechanisms
Motivated by online recommendation systems, we propose the problem of fi...
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01/28/2022
Planning and Learning with Adaptive Lookahead
The classical Policy Iteration (PI) algorithm alternates between greedy ...
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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...
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10/12/2021
Twice regularized MDPs and the equivalence between robustness and regularization
Robust Markov decision processes (MDPs) aim to handle changing or partia...
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10/12/2021
Dare not to Ask: Problem-Dependent Guarantees for Budgeted Bandits
We consider a stochastic multi-armed bandit setting where feedback is li...
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10/07/2021
Reinforcement Learning in Reward-Mixing MDPs
Learning a near optimal policy in a partially observable system remains ...
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10/05/2021
Continuous-Time Fitted Value Iteration for Robust Policies
Solving the Hamilton-Jacobi-Bellman equation is important in many domain...
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10/01/2021
Sim and Real: Better Together
Simulation is used extensively in autonomous systems, particularly in ro...
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09/22/2021
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) faces significant ...
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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...
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05/25/2021
Robust Value Iteration for Continuous Control Tasks
When transferring a control policy from simulation to a physical system,...
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05/10/2021
Value Iteration in Continuous Actions, States and Time
Classical value iteration approaches are not applicable to environments ...
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05/01/2021
Better than the Best: Gradient-based Improper Reinforcement Learning for Network Scheduling
We consider the problem of scheduling in constrained queueing networks w...
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04/06/2021
Using Kalman Filter The Right Way: Noise Estimation Is Not Optimal
Determining the noise parameters of a Kalman Filter (KF) has been resear...
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03/18/2021
Maximum Entropy Reinforcement Learning with Mixture Policies
Mixture models are an expressive hypothesis class that can approximate a...
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02/22/2021
Action Redundancy in Reinforcement Learning
Maximum Entropy (MaxEnt) reinforcement learning is a powerful learning p...
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02/22/2021
GELATO: Geometrically Enriched Latent Model for Offline Reinforcement Learning
Offline reinforcement learning approaches can generally be divided to pr...
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02/18/2021
Reinforcement Learning for Datacenter Congestion Control
We approach the task of network congestion control in datacenters using ...
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02/16/2021
Improper Learning with Gradient-based Policy Optimization
We consider an improper reinforcement learning setting where the learner...
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02/13/2021
Online Apprenticeship Learning
In Apprenticeship Learning (AL), we are given a Markov Decision Process ...
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02/09/2021
RL for Latent MDPs: Regret Guarantees and a Lower Bound
In this work, we consider the regret minimization problem for reinforcem...
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02/07/2021
