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

Scalable Reinforcement Learning via Adaptive Batch Scaling

ICML 2026 · Reinforcement learning › Policy gradient and actor-critic · approach: Regularized scalable deep value learning · 0 citations (OpenAlex)

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

• Stable Gradients for Stable Learning at Scale in Deep Reinforcement Learning (NeurIPS 2025 · similarity 0.88)
• Bigger, Regularized, Optimistic: scaling for compute and sample efficient continuous control (NeurIPS 2024 · similarity 0.88)
• ARS: Adaptive Reward Scaling for Multi-Task Reinforcement Learning (ICML 2025 · similarity 0.88)
• Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity (ICLR 2024 · similarity 0.88)
• Balancing Context Length and Mixing Times for Reinforcement Learning at Scale (NeurIPS 2024 · similarity 0.88)
• On Predictability of Reinforcement Learning Dynamics for Large Language Models (ICLR 2026 · similarity 0.88)