Research map / Reinforcement learning / MDPs and planning
Lipschitz Lifelong Reinforcement Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Transfer value bounds between tasks using task similarity | RMax in lifelong RL · 11×11 grid-world lifelong RL | discounted return | Helped |
| Transfer value bounds between tasks using task similarity | RMax in lifelong RL · 11×11 grid-world lifelong RL | convergence speed | Helped |
| Combine LRMax and MaxQInit bounds | Lifelong RL · 11×11 grid-world lifelong RL | discounted return | Helped |
| Use a tighter prior upper bound on task distance (ablation) | LRMax · 11×11 grid-world lifelong RL | convergence speed | Helped |
| Use a tighter prior upper bound on task distance (ablation) | LRMax · 11×11 grid-world lifelong RL | total return | Helped |
| Combine LRMax and MaxQInit bounds (ablation) | Lifelong RL · 11×11 grid-world lifelong RL | discounted return | Helped |
| Transfer value bounds using LRMax | RMax in lifelong RL · 11×11 grid-world lifelong RL | negative transfer | No clear effect |
Closest papers by meaning
- Policy and Value Transfer in Lifelong Reinforcement Learning (ICML 2018 · similarity 0.91)
- A PAC Learning Algorithm for LTL and Omega-Regular Objectives in MDPs (AAAI 2024 · similarity 0.88)
- Oracle-Efficient Reinforcement Learning for Max Value Ensembles (NeurIPS 2024 · similarity 0.86)
- Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces (NeurIPS 2024 · similarity 0.86)
- Reinforcement Learning with LTL and $\omega$-Regular Objectives via Optimality-Preserving Translation to Average Rewards (NeurIPS 2024 · similarity 0.86)
- Fast TRAC: A Parameter-Free Optimizer for Lifelong Reinforcement Learning (NeurIPS 2024 · similarity 0.86)
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