Emilio Calvano

Reinforcement Learning · 2020

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Emilio Calvano trusts overall

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

  1. Richard S. Sutton
  2. Ido Erev
  3. Michael Wunder
  4. Daan Bloembergen
  5. Andrew Barto
  6. Alvin E. Roth
  7. Ben J. A. Kröse
  8. Michael L. Littman
  9. Karl Tuyls

Papers from Emilio Calvano's perspective

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

  1. Reinforcement Learning: An Introduction
  2. Learning from delayed rewards
  3. Multiagent cooperation and competition with deep reinforcement learning
  4. Evolutionary Dynamics of Multi-Agent Learning: A Survey
  5. Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria
  6. Markov Games as a Framework for Multi-Agent Reinforcement Learning
  7. Classes of Multiagent Q-learning Dynamics with epsilon-greedy Exploration
  8. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
  9. Human-level control through deep reinforcement learning
  10. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)

Their papers