mlsanityData to 7 Oct 2026

Genetic Imitation Learning by Reward Extrapolation

arXiv 2301.07182 ↗First posted 3 Jan 2023cs.NE cs.LGcited by 1

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Imitation learning demonstrates remarkable performance in various domains. However, imitation learning is also constrained by many prerequisites. The research community has done intensive research to alleviate these constraints, such as adding the stochastic policy to avoid unseen states, eliminating the need for action labels, and learning from the suboptimal demonstrations. Inspired by the natural reproduction process, we proposed a method called GenIL that integrates the Genetic Algorithm with imitation learning. The involvement of the Genetic Algorithm improves the data efficiency by reproducing trajectories with various returns and assists the model in estimating more accurate and compact reward function parameters. We tested GenIL in both Atari and Mujoco domains, and the result shows that it successfully outperforms the previous extrapolation methods over extrapolation accuracy, robustness, and overall policy performance when input data is limited.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 21 of its 43 references that are in the map. First posted on arXiv 3 Jan 2023.

Builds on

21 of 43 references.

Closest ancestors · 6

Same direction, newest first

  1. 2021Imitation Learning: Progress, Taxonomies and Challenges23 Jun
  2. 2021Off-Policy Imitation Learning from Observations25 Feb
  3. 2019Goal-conditioned Imitation Learning13 Jun
  4. 2019Causal Confusion in Imitation Learning28 May
  5. 2019Imitation Learning from Video by Leveraging Proprioception22 May
  6. 2019Hindsight Generative Adversarial Imitation Learning19 Mar

Other references · 15

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2017Deep reinforcement learning from human preferencescited by 3,846
  3. 2016OpenAI Gymcited by 1,684
  4. 2016Generative Adversarial Imitation Learningcited by 1,576
  5. 2017Hindsight Experience Replaycited by 1,096
  6. 2017End-to-end Driving via Conditional Imitation Learningcited by 479
  7. 2017Time-Contrastive Networks: Self-Supervised Learning from Videocited by 453
  8. 2019Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observationscited by 256
  9. 2017One-Shot Imitation Learningcited by 232
  10. 2017Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translationcited by 167
  11. 2018Playing hard exploration games by watching YouTubecited by 98
  12. 2019Learning Reward Functions by Integrating Human Demonstrations and Preferencescited by 64
3 more
  1. 2015Learning to Search Better Than Your Teachercited by 50
  2. 2019Inverse Reinforcement Learning with Multiple Ranked Expertscited by 5
  3. 2020Evolutionary Selective Imitation: Interpretable Agents by Imitation Learning Without a Demonstratorcited by 1

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Leveraging Genetic Algorithms for Efficient Demonstration Generation in Real-World Reinforcement Learning Environmentscited by 3

Similar, not cited

Close in wording, no citation either way

  1. 2025Self-evolved Imitation Learning in Simulated World
  2. 2025Beyond-Expert Performance with Limited Demonstrations: Efficient Imitation Learning with Double Exploration
  3. 2019Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations
  4. 2020Intrinsic Reward Driven Imitation Learning via Generative Model
  5. 2019Reward-Conditioned Policies
  6. 2022Imitating, Fast and Slow: Robust learning from demonstrations via decision-time planning
  7. 2019Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation
  8. 2023A Survey of Imitation Learning: Algorithms, Recent Developments, and Challenges