Zobrazeno 1 - 10
of 308
pro vyhledávání: '"Bertsekas, Dimitri P."'
In this paper we apply model predictive control (MPC), rollout, and reinforcement learning (RL) methodologies to computer chess. We introduce a new architecture for move selection, within which available chess engines are used as components. One engi
Externí odkaz:
http://arxiv.org/abs/2409.06477
Autor:
Bertsekas, Dimitri P.
In this paper we describe a new conceptual framework that connects approximate Dynamic Programming (DP), Model Predictive Control (MPC), and Reinforcement Learning (RL). This framework centers around two algorithms, which are designed largely indepen
Externí odkaz:
http://arxiv.org/abs/2406.00592
In this work, we consider data association problems involving multi-object tracking (MOT). In particular, we address the challenges arising from object occlusions. We propose a framework called approximate dynamic programming track (ADPTrack), which
Externí odkaz:
http://arxiv.org/abs/2405.15137
Autor:
Li, Yuchao, Bertsekas, Dimitri
In this paper we consider a transformer with an $n$-gram structure, such as the one underlying ChatGPT. The transformer provides next word probabilities, which can be used to generate word sequences. We consider methods for computing word sequences t
Externí odkaz:
http://arxiv.org/abs/2403.15465
In this paper, we focus on the autonomous multiagent taxi routing problem for a large urban environment where the location and number of future ride requests are unknown a-priori, but can be estimated by an empirical distribution. Recent theory has s
Externí odkaz:
http://arxiv.org/abs/2311.01534
Autor:
Bertsekas, Dimitri
We consider the classical linear assignment problem, and we introduce new auction algorithms for its optimal and suboptimal solution. The algorithms are founded on duality theory, and are related to ideas of competitive bidding by persons for objects
Externí odkaz:
http://arxiv.org/abs/2310.03159
Autor:
Weber, Jamison W., Giriyan, Dhanush R., Parkar, Devendra R., Bertsekas, Dimitri P., Richa, Andréa W.
In this work we consider a generalization of the well-known multivehicle routing problem: given a network, a set of agents occupying a subset of its nodes, and a set of tasks, we seek a minimum cost sequence of movements subject to the constraint tha
Externí odkaz:
http://arxiv.org/abs/2305.15596
Autor:
Bertsekas, Dimitri
We provide a unifying approximate dynamic programming framework that applies to a broad variety of problems involving sequential estimation. We consider first the construction of surrogate cost functions for the purposes of optimization, and we focus
Externí odkaz:
http://arxiv.org/abs/2212.07998
We derive a learning framework to generate routing/pickup policies for a fleet of autonomous vehicles tasked with servicing stochastically appearing requests on a city map. We focus on policies that 1) give rise to coordination amongst the vehicles,
Externí odkaz:
http://arxiv.org/abs/2211.14983
In this paper we address the solution of the popular Wordle puzzle, using new reinforcement learning methods, which apply more generally to adaptive control of dynamic systems and to classes of Partially Observable Markov Decision Process (POMDP) pro
Externí odkaz:
http://arxiv.org/abs/2211.10298