Research map / Reinforcement learning / Policy gradient and actor-critic

Sample-efficient and Scalable Exploration in Continuous-Time RL

ICLR 2026 · Reinforcement learning › Policy gradient and actor-critic · approach: Model-based RL with guarantees

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

• Efficient Exploration in Continuous-time Model-based Reinforcement Learning (NeurIPS 2023 · similarity 0.93)
• NeoRL: Efficient Exploration for Nonepisodic RL (NeurIPS 2024 · similarity 0.92)
• Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds (ICLR 2025 · similarity 0.91)
• SOMBRL: Scalable and Optimistic Model-Based RL (NeurIPS 2025 · similarity 0.91)
• Sampling-Based Safe Reinforcement Learning (NeurIPS 2026 · similarity 0.90)
• MetaCURL: Non-stationary Concave Utility Reinforcement Learning (NeurIPS 2024 · similarity 0.89)