Yang Jun
Reinforcement Learning · 2021
A researcher in the Science corpus: 1 paper; trusts 4 researchers and is trusted by 0 researchers here, as inferred from the citation graph.
Subfields
- Reinforcement Learning — 23 citations into it
Whom Yang Jun trusts
Their strongest outbound trust, strongest first, inferred from their citations.
- Ronald J. Williams (in Reinforcement Learning)
- Fei Sha (in Reinforcement Learning)
- Shariq Iqbal (in Reinforcement Learning)
- Shimon Whiteson (in Reinforcement Learning)
Papers from Yang Jun's perspective
Ordered by how strongly the researchers Yang Jun trusts endorse each paper. Same corpus, a different order for every researcher.
- Attention is All you Need (Neural Information Processing Systems)
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning
- Unifying Count-Based Exploration and Intrinsic Motivation
- Counterfactual Multi-Agent Policy Gradients (AAAI Conference on Artificial Intelligence)
- Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (International Conference on Machine Learning)
- Deep Exploration via Bootstrapped DQN
- Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
- VIME: Variational Information Maximizing Exploration
- Control of Memory, Active Perception, and Action in Minecraft
- QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning