Jeffrey Tsang

Reinforcement Learning · 2017

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 Jeffrey Tsang trusts

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

  1. Richard S. Sutton (in Reinforcement Learning)
  2. Andrew Barto (in Reinforcement Learning)

Papers from Jeffrey Tsang's perspective

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

  1. Reinforcement Learning: An Introduction
  2. Asynchronous Methods for Deep Reinforcement Learning
  3. Human-level control through deep reinforcement learning
  4. Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
  5. The Arcade Learning Environment: An Evaluation Platform for General Agents
  6. Reinforcement Learning with Unsupervised Auxiliary Tasks
  7. Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping
  8. Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
  9. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation
  10. The Option-Critic Architecture

Their papers