Skip to main navigation
Skip to search
Skip to main content
Arizona State University Home
Home
Profiles
Departments and Centers
Scholarly Works
Activities
Equipment
Grants
Datasets
Prizes
Search by expertise, name or affiliation
Multiagent Reinforcement Learning: Rollout and Policy Iteration
Dimitri Bertsekas
Computer Science and Engineering
Research output
:
Contribution to journal
›
Article
›
peer-review
57
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Multiagent Reinforcement Learning: Rollout and Policy Iteration'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Mathematics
Multiagent Learning
100%
Reinforcement Learning
72%
Policy Iteration
70%
Policy
47%
Space Complexity
9%
Optimal Policy
8%
State Space
6%
Costs
5%
Infinite-horizon Problems
5%
Local Algorithms
5%
State Complexity
5%
Iteration
4%
Standards
4%
Trade-offs
3%
Reformulation
3%
Decision problem
3%
Neural Networks
3%
Robustness
3%
Objective function
3%
Form
3%
Linearly
2%
Strictly
2%
Sufficient
2%
Line
2%
Performance
2%
Class
1%
Engineering & Materials Science
Reinforcement learning
48%
Costs
3%
Neural networks
2%