Research map / Reinforcement learning / Policy gradient and actor-critic
Pink Noise Is All You Need: Colored Noise Exploration in Deep Reinforcement Learning
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- Colored Noise in PPO: Improved Exploration and Performance through Correlated Action Sampling (AAAI 2024 · similarity 0.93)
- Learning to Explore with Parameter-Space Noise: A Deep Dive into Parameter-Space Noise for Reinforcement Learning with Verifiable Rewards (NeurIPS 2026 · similarity 0.86)
- Unified Noise Steering for Efficient Human-Guided VLA Adaptation (NeurIPS 2026 · similarity 0.84)
- Emergence of In-Context Reinforcement Learning from Noise Distillation (ICML 2024 · similarity 0.84)
- Noise-Resilient Symbolic Regression with Dynamic Gating Reinforcement Learning (AAAI 2025 · similarity 0.83)
- Butterfly Effects of SGD Noise: Error Amplification in Behavior Cloning and Autoregression (ICLR 2024 · similarity 0.83)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.
