Minne Li

Reinforcement Learning · 2018

A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 25 citations into it
• Game & Decision Theory — 6 citations into it

Whom Minne Li trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Michael L. Littman
2. Csaba Szepesvari
3. Michael H. Bowling
4. Richard S. Sutton
5. Satinder Singh

Papers from Minne Li's perspective

Ordered by how strongly the researchers Minne Li trusts endorse each paper. Same corpus, a different order for every researcher.

1. Markov Games as a Framework for Multi-Agent Reinforcement Learning
2. Deterministic Policy Gradient Algorithms
3. Q-learning
4. Reinforcement Learning: An Introduction
5. ImageNet classification with deep convolutional neural networks
6. Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms
7. Multi Agent Reinforcement Learning Independent vs Cooperative Agents
8. Actor-Critic Algorithms
9. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
10. Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Their papers

• Mean Field Multi-Agent Reinforcement Learning (2018) — 717 citations