mlsanityData to 14 Oct 2026

A Survey on Reinforcement Learning Methods in Character Animation

arXiv 2203.04735 ↗First posted 7 Mar 2022cs.GR cs.LGcited by 12

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Abstract (arXiv)

Reinforcement Learning is an area of Machine Learning focused on how agents can be trained to make sequential decisions, and achieve a particular goal within an arbitrary environment. While learning, they repeatedly take actions based on their observation of the environment, and receive appropriate rewards which define the objective. This experience is then used to progressively improve the policy controlling the agent's behavior, typically represented by a neural network. This trained module can then be reused for similar problems, which makes this approach promising for the animation of autonomous, yet reactive characters in simulators, video games or virtual reality environments. This paper surveys the modern Deep Reinforcement Learning methods and discusses their possible applications in Character Animation, from skeletal control of a single, physically-based character to navigation controllers for individual agents and virtual crowds. It also describes the practical side of training DRL systems, comparing the different frameworks available to build such agents.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 73 of its 172 references that are in the map. First posted on arXiv 7 Mar 2022.

Builds on

73 of 172 references.

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50 most cited of 73 All 73 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 4; 2024: 2; 2025: 4; 2026: 1.

12 papers.

  1. 2023MoConVQ: Unified Physics-Based Motion Control via Scalable Discrete Representationscited by 60
  2. 2025Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Modelscited by 56
  3. 2024Programmable Motion Generation for Open-Set Motion Control Taskscited by 20
  4. 2023AdaptNet: Policy Adaptation for Physics-Based Character Controlcited by 16
  5. 2023Physics-based Motion Retargeting from Sparse Inputscited by 10
  6. 2024LEAD: Latent Realignment for Human Motion Diffusioncited by 9
  7. 20253D Human Interaction Generation: A Surveycited by 7
  8. 2025Learning to Ball: Composing Policies for Long-Horizon Basketball Movescited by 6
  9. 2022Understanding reinforcement learned crowdscited by 2
  10. 2025Environment-aware Motion Matchingcited by 1
  11. 2023Reward Function Design for Crowd Simulation via Reinforcement Learningcited by 0
  12. 2026Learning High-Risk High-Precision Motion Controlcited by 0