41 citations · 160 across the 25 of their papers we have counts for
29 papers
Model Predictive Control and Reinforcement Learning: A Unified Framework Based on Dynamic Programming
Dimitri P. Bertsekas
In this paper we describe a new conceptual framework that connects approximate Dynamic Programming (DP), Model Predictive Control (MPC), and Reinforcement Learning (RL). This frame…
Distributed Online Rollout for Multivehicle Routing in Unmapped Environments
Jamison W. Weber, Dhanush R. Giriyan, Devendra R. Parkar +2
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 s…
Rollout Algorithms and Approximate Dynamic Programming for Bayesian Optimization and Sequential Estimation
Dimitri Bertsekas
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 s…
Reinforcement Learning Methods for Wordle: A POMDP/Adaptive Control Approach
Siddhant Bhambri, Amrita Bhattacharjee, Dimitri Bertsekas
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…
Multiagent Reinforcement Learning for Autonomous Routing and Pickup Problem with Adaptation to Variable Demand
Daniel Garces, Sushmita Bhattacharya, Stephanie Gil +1
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 focu…
New Auction Algorithms for Path Planning, Network Transport, and Reinforcement Learning
Dimitri Bertsekas
We consider some classical optimization problems in path planning and network transport, and we introduce new auction-based algorithms for their optimal and suboptimal solution. Th…