Bart De Vylder

Reinforcement Learning · 2019–2020

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 62 citations into it
• Game & Decision Theory — 59 citations into it

Whom Bart De Vylder trusts

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

1. Michael L. Littman (in Reinforcement Learning)
2. David Silver (in Reinforcement Learning)
3. Marc Lanctot (in Reinforcement Learning)

Papers from Bart De Vylder's perspective

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

1. Reinforcement Learning: An Introduction
2. Markov Games as a Framework for Multi-Agent Reinforcement Learning
3. Asynchronous Methods for Deep Reinforcement Learning
4. The Theory of Learning in Games
5. Fast algorithms for finding randomized strategies in game trees
6. The Arcade Learning Environment: An Evaluation Platform for General Agents
7. Human-level control through deep reinforcement learning
8. Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents
9. TensorFlow: A system for large-scale machine learning
10. Bandit Based Monte-Carlo Planning

Their papers

• From Poincaré Recurrence to Convergence in Imperfect Information Games: Finding Equilibrium via Regularization (2020) — 107 citations
• OpenSpiel: A Framework for Reinforcement Learning in Games (2019) — 323 citations