Research map / Reinforcement learning / Policy gradient and actor-critic
Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration
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
- Task-agnostic Exploration in Reinforcement Learning (NeurIPS 2020 · similarity 0.91)
- Exploration-Driven Policy Optimization in RLHF: Theoretical Insights on Efficient Data Utilization (ICML 2024 · similarity 0.90)
- Reward-Free Kernel-Based Reinforcement Learning (ICML 2024 · similarity 0.89)
- MaxInfoRL: Boosting exploration in reinforcement learning through information gain maximization (ICLR 2025 · similarity 0.89)
- Multi-Reward Best Policy Identification (NeurIPS 2024 · similarity 0.89)
- Exploration from a Primal-Dual Lens: Value-Incentivized Actor-Critic Methods for Sample-Efficient Online RL (NeurIPS 2025 · similarity 0.88)
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.
