Research map / Reinforcement learning / MDPs and planning

Optimistic Exploration with Learned Features Provably Solves Markov Decision Processes with Neural Dynamics

ICLR 2023 · Reinforcement learning › MDPs and planning · approach: Offline RL sample complexity

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

• PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration (ICML 2021 · similarity 0.89)
• Learning Markov State Abstractions for Deep Reinforcement Learning (NeurIPS 2021 · similarity 0.88)
• From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments (ICLR 2026 · similarity 0.88)
• Explicit Explore-Exploit Algorithms in Continuous State Spaces (NeurIPS 2019 · similarity 0.88)
• Sample-efficient and Scalable Exploration in Continuous-Time RL (ICLR 2026 · similarity 0.88)
• Exploiting the Replay Memory Before Exploring the Environment: Enhancing Reinforcement Learning Through Empirical MDP Iteration (NeurIPS 2024 · similarity 0.87)