Chang Ye

Reinforcement Learning · 2022–2025

A researcher in the Science corpus: 2 papers; trusts 5 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

• Reinforcement Learning — 17 citations into it
• Artificial Intelligence — 13 citations into it

Whom Chang Ye trusts overall

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

1. E. Todorov
2. John Schulman
3. Shengyi Huang
4. Marc G. Bellemare

Papers from Chang Ye's perspective

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

1. Proximal Policy Optimization Algorithms (arXiv.org)
2. Continuous control with deep reinforcement learning (International Conference on Learning Representations)
3. Playing Atari with Deep Reinforcement Learning (arXiv.org)
4. MuJoCo: A physics engine for model-based control
5. The Arcade Learning Environment: An Evaluation Platform for General Agents
6. OpenAI Gym
7. Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor (International Conference on Machine Learning)
8. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
9. ImageNet classification with deep convolutional neural networks
10. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)

Their papers

• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• CleanRL: High-quality Single-file Implementations of Deep Reinforcement Learning Algorithms (2022) — 510 citations