10 citations · 10 across the 3 of their papers we have counts for
6 papers
Multiagent Value Iteration Algorithms in Dynamic Programming and Reinforcement Learning
Dimitri Bertsekas
We consider infinite horizon dynamic programming problems, where the control at each stage consists of several distinct decisions, each one made by one of several agents. In an ear…
Reinforcement Learning for POMDP: Partitioned Rollout and Policy Iteration with Application to Autonomous Sequential Repair Problems
Sushmita Bhattacharya, Sahil Badyal, Thomas Wheeler +2
In this paper we consider infinite horizon discounted dynamic programming problems with finite state and control spaces, and partial state observations. We discuss an algorithm tha…
Constrained Multiagent Rollout and Multidimensional Assignment with the Auction Algorithm
Dimitri Bertsekas
We consider an extension of the rollout algorithm that applies to constrained deterministic dynamic programming, including challenging combinatorial optimization problems. The algo…
Biased Aggregation, Rollout, and Enhanced Policy Improvement for Reinforcement Learning
Dimitri Bertsekas
We propose a new aggregation framework for approximate dynamic programming, which provides a connection with rollout algorithms, approximate policy iteration, and other single and…
Multiagent Rollout Algorithms and Reinforcement Learning
Dimitri Bertsekas
We consider finite and infinite horizon dynamic programming problems, where the control at each stage consists of several distinct decisions, each one made by one of several agents…
Stable Optimal Control and Semicontractive Dynamic Programming
Dimitri P. Bertsekas
We consider discrete-time infinite horizon deterministic optimal control problems with nonnegative cost per stage, and a destination that is cost-free and absorbing. The classical…