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Found 21 papers, 4 papers with code
Stereo Event-based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction
Existing Particle Imaging Velocimetry techniques require the use of high-speed cameras to reconstruct time-resolved fluid flows.
Is RLHF More Difficult than Standard RL?
This paper theoretically proves that, for a wide range of preference models, we can solve preference-based RL directly using existing algorithms and techniques for reward-based RL, with small or no extra costs.
Breaking the Curse of Multiagency: Provably Efficient Decentralized Multi-Agent RL with Function Approximation
A unique challenge in Multi-Agent Reinforcement Learning (MARL) is the curse of multiagency, where the description length of the game as well as the complexity of many existing learning algorithms scale exponentially with the number of agents.
Learning Rationalizable Equilibria in Multiplayer Games
This paper develops the first line of efficient algorithms for learning rationalizable Coarse Correlated Equilibria (CCE) and Correlated Equilibria (CE) whose sample complexities are polynomial in all problem parameters including the number of players.
Learning Markov Games with Adversarial Opponents: Efficient Algorithms and Fundamental Limits
When the policies of the opponents are not revealed, we prove a statistical hardness result even in the most favorable scenario when both above conditions are true.
Neural Adaptive SCEne Tracing
Neural rendering with implicit neural networks has recently emerged as an attractive proposition for scene reconstruction, achieving excellent quality albeit at high computational cost.
NeAT: Neural Adaptive Tomography
Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods.
V-Learning -- A Simple, Efficient, Decentralized Algorithm for Multiagent RL
We design a new class of fully decentralized algorithms -- V-learning, which provably learns Nash equilibria (in the two-player zero-sum setting), correlated equilibria and coarse correlated equilibria (in the multiplayer general-sum setting) in a number of samples that only scales with $\max_{i\in[m]} A_i$, where $A_i$ is the number of actions for the $i^{\rm th}$ player.
An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap
The recent and remarkable result of Weisz et al. (2020) resolves this question in the negative, providing an exponential (in $d$) sample size lower bound, which holds even if the agent has access to a generative model of the environment.
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap
This work focuses on this question in the standard online reinforcement learning setting, where our main result resolves this question in the negative: our hardness result shows that an exponential sample complexity lower bound still holds even if a constant suboptimality gap is assumed in addition to having a linearly realizable optimal $Q$-function.
Near-optimal Local Convergence of Alternating Gradient Descent-Ascent for Minimax Optimization
Smooth minimax games often proceed by simultaneous or alternating gradient updates.
Online Learning in Unknown Markov Games
We study online learning in unknown Markov games, a problem that arises in episodic multi-agent reinforcement learning where the actions of the opponents are unobservable.
Refined Analysis of FPL for Adversarial Markov Decision Processes
We consider the adversarial Markov Decision Process (MDP) problem, where the rewards for the MDP can be adversarially chosen, and the transition function can be either known or unknown.
On the Suboptimality of Negative Momentum for Minimax Optimization
Smooth game optimization has recently attracted great interest in machine learning as it generalizes the single-objective optimization paradigm.
Improved Algorithms for Convex-Concave Minimax Optimization
This paper studies minimax optimization problems $\min_x \max_y f(x, y)$, where $f(x, y)$ is $m_x$-strongly convex with respect to $x$, $m_y$-strongly concave with respect to $y$ and $(L_x, L_{xy}, L_y)$-smooth.
On Solving Minimax Optimization Locally: A Follow-the-Ridge Approach
Many tasks in modern machine learning can be formulated as finding equilibria in \emph{sequential} games.
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.
Distributed Bandit Learning: Near-Optimal Regret with Efficient Communication
We study the problem of regret minimization for distributed bandits learning, in which $M$ agents work collaboratively to minimize their total regret under the coordination of a central server.
Q-learning with UCB Exploration is Sample Efficient for Infinite-Horizon MDP
A fundamental question in reinforcement learning is whether model-free algorithms are sample efficient.
In-Orbit Instrument Performance Study and Calibration for POLAR Polarization Measurements
POLAR is a compact space-borne detector designed to perform reliable measurements of the polarization for transient sources like Gamma-Ray Bursts in the energy range 50-500keV.
Instrumentation and Methods for Astrophysics High Energy Physics - Experiment Instrumentation and Detectors
16-qubit IBM universal quantum computer can be fully entangled
Entanglement is an important evidence that a quantum device can potentially solve problems intractable for classical computers.
Quantum Physics
