Toby Boyd

Reinforcement Learning · 2021

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

Whom Toby Boyd trusts

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

  1. Longxin Lin (in Reinforcement Learning)

Papers from Toby Boyd's perspective

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

  1. Human-level control through deep reinforcement learning
  2. Proximal Policy Optimization Algorithms (arXiv.org)
  3. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
  4. Distributed Prioritized Experience Replay
  5. Grandmaster level in StarCraft II using multi-agent reinforcement learning
  6. Self-improving reactive agents based on reinforcement learning, planning and teaching
  7. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (International Conference on Machine Learning)
  8. The Reactor: A Sample-Efficient Actor-Critic Architecture
  9. Acme: A Research Framework for Distributed Reinforcement Learning
  10. Revisiting Fundamentals of Experience Replay (International Conference on Machine Learning)

Their papers