# Planning with RL and episodic-memory behavioral priors

arXiv: https://arxiv.org/abs/2207.01845 · HTML: https://mlsanity.com/paper/2207.01845/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 18 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Recent Advances in Imitation Learning from Observation](https://mlsanity.com/paper/1905.13566/) (2019)
- [Simple and Effective VAE Training with Calibrated Decoders](https://mlsanity.com/paper/2006.13202/) (2020)
- [Learn-to-Race: A Multimodal Control Environment for Autonomous Racing](https://mlsanity.com/paper/2103.11575/) (2021)
- [Critic Guided Segmentation of Rewarding Objects in First-Person Views](https://mlsanity.com/paper/2107.09540/) (2021)
- [Solving Physics Puzzles by Reasoning about Paths](https://mlsanity.com/paper/2011.07357/) (2020)
- [Modularization of End-to-End Learning: Case Study in Arcade Games](https://mlsanity.com/paper/1901.09895/) (2019)
- [Solving Learn-to-Race Autonomous Racing Challenge by Planning in Latent Space](https://mlsanity.com/paper/2207.01275/) (2022)

## Built on by (most cited first, 8 of 8)

- [Social World Models](https://mlsanity.com/paper/2509.00559/) (2025)
- [UniTeam: Open Vocabulary Mobile Manipulation Challenge](https://mlsanity.com/paper/2312.08611/) (2023)
- [Zero-shot Imitation Policy via Search in Demonstration Dataset](https://mlsanity.com/paper/2401.16398/) (2024)
- [Behavioral Cloning via Search in Video PreTraining Latent Space](https://mlsanity.com/paper/2212.13326/) (2022)
- [Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks](https://mlsanity.com/paper/2312.05925/) (2023)
- [Stroke-based Rendering: From Heuristics to Deep Learning](https://mlsanity.com/paper/2302.00595/) (2022)
- [Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs](https://mlsanity.com/paper/2403.13801/) (2024)
- [Behavioral Cloning via Search in Embedded Demonstration Dataset](https://mlsanity.com/paper/2306.09082/) (2023)

## Abstract (arXiv)

The practical application of learning agents requires sample efficient and interpretable algorithms. Learning from behavioral priors is a promising way to bootstrap agents with a better-than-random exploration policy or a safe-guard against the pitfalls of early learning. Existing solutions for imitation learning require a large number of expert demonstrations and rely on hard-to-interpret learning methods like Deep Q-learning. In this work we present a planning-based approach that can use these behavioral priors for effective exploration and learning in a reinforcement learning environment, and we demonstrate that curated exploration policies in the form of behavioral priors can help an agent learn faster.