mlsanityData to 14 Oct 2026

Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping

arXiv 2312.09983 ↗First posted 15 Dec 2023cs.LG cs.AI stat.MLcited by 2

On the map: Offline RL and imitation learning from data → Inverse reinforcement learning

Abstract (arXiv)

Inverse reinforcement learning (IRL) is computationally challenging, with common approaches requiring the solution of multiple reinforcement learning (RL) sub-problems. This work motivates the use of potential-based reward shaping to reduce the computational burden of each RL sub-problem. This work serves as a proof-of-concept and we hope will inspire future developments towards computationally efficient IRL.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Inverse reinforcement learning, one of 13 directions in Offline RL and imitation learning from data. Builds on 9 of its 21 references that are in the map. First posted on arXiv 15 Dec 2023.

Builds on

9 of 21 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Receding Horizon Inverse Reinforcement Learning9 Jun
  2. 2021Identifiability in inverse reinforcement learning7 Jun

Other references · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Maximum Entropy Deep Inverse Reinforcement Learningcited by 176
  3. 2020Learning to Utilize Shaping Rewards: A New Approach of Reward Shapingcited by 114
  4. 2022Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexitycited by 62
  5. 2021Heuristic-Guided Reinforcement Learningcited by 42
  6. 2023Bridging RL Theory and Practice with the Effective Horizoncited by 31
  7. 2023Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learningcited by 5

Built on by

Cited by year

Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024EvIL: Evolution Strategies for Generalisable Imitation Learningcited by 8
  2. 2025Bootstrapped Reward Shapingcited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2018A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
  2. 2023Is Inverse Reinforcement Learning Harder than Standard Reinforcement Learning? A Theoretical Perspective
  3. 2024Offline Inverse RL: New Solution Concepts and Provably Efficient Algorithms
  4. 2025Reward Compatibility: A Framework for Inverse RL
  5. 2026Inverse Reinforcement Learning without an Optimal Demonstrator: A Feasible Reward Set Approach
  6. 2023Towards Theoretical Understanding of Inverse Reinforcement Learning
  7. 2020Reward Shaping for Human Learning via Inverse Reinforcement Learning
  8. 2019Option Compatible Reward Inverse Reinforcement Learning