Research map / Bandits and online learning / RL theory and MDPs
Optimism in Reinforcement Learning with Generalized Linear Function Approximation
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use LSVI-UCB with generalized linear function approximation under optimistic closure | Episodic reinforcement learning · Episodic MDPs with generalized linear function approximation and optimistic closure | cumulative regret: \tilde{O}(H\sqrt{d^3T}) | Helped |
| Use standard-basis features and the identity link function | LSVI-UCB · Finite-state, finite-action MDPs | cumulative regret: \tilde{O}(H\sqrt{|\Scal|^3|\Acal|^3 T}) | Mixed |
| Replace explicit linear-MDP dynamics assumptions with optimistic closure | LSVI-UCB analysis · Reinforcement learning with function approximation | assumption strength | Helped |
Closest papers by meaning
- A Unifying View of Optimism in Episodic Reinforcement Learning (NeurIPS 2020 · similarity 0.92)
- Randomized Exploration in Reinforcement Learning with General Value Function Approximation (ICML 2021 · similarity 0.91)
- Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations (NeurIPS 2020 · similarity 0.91)
- Minimax Optimal Reinforcement Learning with Quasi-Optimism (ICLR 2025 · similarity 0.90)
- Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning (ICLR 2024 · similarity 0.89)
- Kernel-Based Function Approximation for Average Reward Reinforcement Learning: An Optimist No-Regret Algorithm (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.