# BIMRL: Brain Inspired Meta Reinforcement Learning

arXiv: https://arxiv.org/abs/2210.16530 · HTML: https://mlsanity.com/paper/2210.16530/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 29 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [Learning to reinforcement learn](https://mlsanity.com/paper/1611.05763/) (2016)
- [Recurrent Independent Mechanisms](https://mlsanity.com/paper/1909.10893/) (2019)
- [VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning](https://mlsanity.com/paper/1910.08348/) (2019)
- [Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules](https://mlsanity.com/paper/2006.16981/) (2020)
- [BeBold: Exploration Beyond the Boundary of Explored Regions](https://mlsanity.com/paper/2012.08621/) (2020)
- [Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning](https://mlsanity.com/paper/2010.01062/) (2020)
- [On the Reliability and Generalizability of Brain-inspired Reinforcement Learning Algorithms](https://mlsanity.com/paper/2007.04578/) (2020)

## Built on by (most cited first, 2 of 2)

- [Inductive Biases for Zero-shot Systematic Generalization in Language-informed Reinforcement Learning](https://mlsanity.com/paper/2501.15270/) (2025)
- [CosmoCore Affective Dream-Replay Reinforcement Learning for Code Generation](https://mlsanity.com/paper/2510.18895/) (2025)

## Abstract (arXiv)

Sample efficiency has been a key issue in reinforcement learning (RL). An efficient agent must be able to leverage its prior experiences to quickly adapt to similar, but new tasks and situations. Meta-RL is one attempt at formalizing and addressing this issue. Inspired by recent progress in meta-RL, we introduce BIMRL, a novel multi-layer architecture along with a novel brain-inspired memory module that will help agents quickly adapt to new tasks within a few episodes. We also utilize this memory module to design a novel intrinsic reward that will guide the agent's exploration. Our architecture is inspired by findings in cognitive neuroscience and is compatible with the knowledge on connectivity and functionality of different regions in the brain. We empirically validate the effectiveness of our proposed method by competing with or surpassing the performance of some strong baselines on multiple MiniGrid environments.