Challenges of real-world reinforcement learning: definitions, benchmarks and analysis
From MaRDI portal
Publication:2071388
DOI10.1007/s10994-021-05961-4OpenAlexW3121342653WikidataQ114224937 ScholiaQ114224937MaRDI QIDQ2071388
Nir Levine, Sven Gowal, Todd Hester, Cosmin Paduraru, Daniel J. Mankowitz, Gabriel Dulac-Arnold, Jerry Li
Publication date: 28 January 2022
Published in: Machine Learning (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10994-021-05961-4
Related Items (6)
Planning for potential: efficient safe reinforcement learning ⋮ Learning reward machines: a study in partially observable reinforcement learning ⋮ Reward (Mis)design for autonomous driving ⋮ Data-driven passivity-based control of underactuated mechanical systems via interconnection and damping assignment ⋮ Safety-constrained reinforcement learning with a distributional safety critic ⋮ Online Bootstrap Inference For Policy Evaluation In Reinforcement Learning
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- \(T_{\mathcal{P}}\)-compilation for inference in probabilistic logic programs
- Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
- A Survey of Multi-Objective Sequential Decision-Making
- Multiple Model-Based Reinforcement Learning
- From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning
- 10.1162/1532443041827907
- Robust Dynamic Programming
This page was built for publication: Challenges of real-world reinforcement learning: definitions, benchmarks and analysis