# Behavior Priors for Efficient Reinforcement Learning

arXiv: https://arxiv.org/abs/2010.14274 · HTML: https://mlsanity.com/paper/2010.14274/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 30 of 41)

- [Learning Agile Soccer Skills for a Bipedal Robot with Deep Reinforcement Learning](https://mlsanity.com/paper/2304.13653/) (2023)
- [Policy Expansion for Bridging Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2302.00935/) (2023)
- [From Motor Control to Team Play in Simulated Humanoid Football](https://mlsanity.com/paper/2105.12196/) (2021)
- [Reward is enough for convex MDPs](https://mlsanity.com/paper/2106.00661/) (2021)
- [Can Wikipedia Help Offline Reinforcement Learning?](https://mlsanity.com/paper/2201.12122/) (2022)
- [Neural Production Systems: Learning Rule-Governed Visual Dynamics](https://mlsanity.com/paper/2103.01937/) (2021)
- [Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models](https://mlsanity.com/paper/2308.15143/) (2023)
- [Imitate and Repurpose: Learning Reusable Robot Movement Skills From Human and Animal Behaviors](https://mlsanity.com/paper/2203.17138/) (2022)
- [Accelerating Exploration with Unlabeled Prior Data](https://mlsanity.com/paper/2311.05067/) (2023)
- [Collect & Infer -- a fresh look at data-efficient Reinforcement Learning](https://mlsanity.com/paper/2108.10273/) (2021)
- [Learning Robot Soccer from Egocentric Vision with Deep Reinforcement Learning](https://mlsanity.com/paper/2405.02425/) (2024)
- [Prior-Guided Diffusion Planning for Offline Reinforcement Learning](https://mlsanity.com/paper/2505.10881/) (2025)
- [Generative Planning for Temporally Coordinated Exploration in Reinforcement Learning](https://mlsanity.com/paper/2201.09765/) (2022)
- [How to Spend Your Robot Time: Bridging Kickstarting and Offline Reinforcement Learning for Vision-based Robotic Manipulation](https://mlsanity.com/paper/2205.03353/) (2022)
- [Proximal Policy Distillation](https://mlsanity.com/paper/2407.15134/) (2024)
- [SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration](https://mlsanity.com/paper/2211.13743/) (2022)
- [Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains](https://mlsanity.com/paper/2302.12617/) (2023)
- [Data augmentation for efficient learning from parametric experts](https://mlsanity.com/paper/2205.11448/) (2022)
- [Analytical Survey of Learning with Low-Resource Data: From Analysis to Investigation](https://mlsanity.com/paper/2510.08962/) (2025)
- [IMWM: Intuition Models Complement World Models for Latent Planning](https://mlsanity.com/paper/2606.01626/) (2026)
- [Priors, Hierarchy, and Information Asymmetry for Skill Transfer in Reinforcement Learning](https://mlsanity.com/paper/2201.08115/) (2022)
- [SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning](https://mlsanity.com/paper/2205.13528/) (2022)
- [Leveraging Demonstrations with Latent Space Priors](https://mlsanity.com/paper/2210.14685/) (2022)
- [Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition](https://mlsanity.com/paper/2302.04944/) (2023)
- [Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions](https://mlsanity.com/paper/2309.10980/) (2023)
- [A Simple Approach to Continual Learning by Transferring Skill Parameters](https://mlsanity.com/paper/2110.10255/) (2021)
- [Efficient Learning of High Level Plans from Play](https://mlsanity.com/paper/2303.09628/) (2023)
- [Goal-Reaching Policy Learning from Non-Expert Observations via Effective Subgoal Guidance](https://mlsanity.com/paper/2409.03996/) (2024)
- [A KL-regularization Framework for Learning to Plan with Adaptive Priors](https://mlsanity.com/paper/2510.04280/) (2025)
- [MIMIC-MJX: Neuromechanical Emulation of Animal Behavior](https://mlsanity.com/paper/2511.20532/) (2025)

## Abstract (arXiv)

As we deploy reinforcement learning agents to solve increasingly challenging problems, methods that allow us to inject prior knowledge about the structure of the world and effective solution strategies becomes increasingly important. In this work we consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors that capture the common movement and interaction patterns that are shared across a set of related tasks or contexts. For example the day-to day behavior of humans comprises distinctive locomotion and manipulation patterns that recur across many different situations and goals. We discuss how such behavior patterns can be captured using probabilistic trajectory models and how these can be integrated effectively into reinforcement learning schemes, e.g. to facilitate multi-task and transfer learning. We then extend these ideas to latent variable models and consider a formulation to learn hierarchical priors that capture different aspects of the behavior in reusable modules. We discuss how such latent variable formulations connect to related work on hierarchical reinforcement learning (HRL) and mutual information and curiosity based objectives, thereby offering an alternative perspective on existing ideas. We demonstrate the effectiveness of our framework by applying it to a range of simulated continuous control domains.