Research map / Reinforcement learning / Policy gradient and actor-critic
Regularization Matters in Policy Optimization - An Empirical Study on Continuous Control
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
- Learning to Trust Bellman Updates: Selective State-Adaptive Regularization for Offline RL (ICML 2025 · similarity 0.90)
- Decoupling regularization from the action space (ICLR 2024 · similarity 0.90)
- State Entropy Regularization for Robust Reinforcement Learning (NeurIPS 2025 · similarity 0.89)
- Demonstration-Regularized RL (ICLR 2024 · similarity 0.89)
- Iteratively Refined Behavior Regularization for Offline Reinforcement Learning (NeurIPS 2024 · similarity 0.89)
- Parseval Regularization for Continual Reinforcement Learning (NeurIPS 2024 · similarity 0.89)
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.
