Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees
On the map: Offline RL and imitation learning from data → Inverse reinforcement learning
Abstract (arXiv)
Inverse Reinforcement Learning (IRL) is the problem of finding a reward function which describes observed/known expert behavior. The IRL setting is remarkably useful for automated control, in situations where the reward function is difficult to specify manually or as a means to extract agent preference. In this work, we provide a new IRL algorithm for the continuous state space setting with unknown transition dynamics by modeling the system using a basis of orthonormal functions. Moreover, we provide a proof of correctness and formal guarantees on the sample and time complexity of our algorithm. Finally, we present synthetic experiments to corroborate our theoretical guarantees.
Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 3 of its 20 references that are in the map. First posted on arXiv 16 Feb 2021.
Builds on
3 of 20 references.
Closest ancestors · 2
Same direction, newest first
- 2019On the Correctness and Sample Complexity of Inverse Reinforcement Learning2 Jun
- 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress18 Jun
Other references · 1
Built on by
Citing papers per year: 2022: 1; 2023: 2; 2024: 5.
8 papers.
- 2022Active Exploration for Inverse Reinforcement Learningcited by 27
- 2023Towards Theoretical Understanding of Inverse Reinforcement Learningcited by 21
- 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approachcited by 5
- 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspectivecited by 4
- 2024Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Reviewcited by 3
- 2024Automated Feature Selection for Inverse Reinforcement Learningcited by 1
- 2024Continuously evolving rewards in an open-ended environmentcited by 1
- 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spacescited by 0
- 2024Towards Sample-Efficiency and Generalization of Transfer and Inverse Reinforcement Learning: A Comprehensive Literature Review15 Nov 2024
- 2024How does Inverse RL Scale to Large State Spaces? A Provably Efficient Approach6 Jun 2024
- 2024Randomized algorithms and PAC bounds for inverse reinforcement learning in continuous spaces24 May 2024
- 2024Continuously evolving rewards in an open-ended environment2 May 2024
- 2024Automated Feature Selection for Inverse Reinforcement Learning22 Mar 2024
- 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective29 Nov 2023
- 2023Towards Theoretical Understanding of Inverse Reinforcement Learning25 Apr 2023
- 2022Active Exploration for Inverse Reinforcement Learning18 Jul 2022
Similar, not cited
Close in wording, no citation either way
- 2023Inverse Reinforcement Learning without Reinforcement Learning
- 2018A Framework and Method for Online Inverse Reinforcement Learning
- 2024Stable Inverse Reinforcement Learning: Policies from Control Lyapunov Landscapes
- 2021Inverse Reinforcement Learning: A Control Lyapunov Approach
- 2025Inverse Reinforcement Learning via Convex Optimization
- 2023A Novel Variational Lower Bound for Inverse Reinforcement Learning
- 2022Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees
- 2025Data-Driven Inverse Optimal Control for Continuous-Time Nonlinear Systems