# Uncertainty Maximization in Partially Observable Domains: A Cognitive Perspective

arXiv: https://arxiv.org/abs/2102.11232 · HTML: https://mlsanity.com/paper/2102.11232/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 91 references that are in the map.
- First posted on arXiv 22 Feb 2021.

## Builds on (most cited first, 12 of 12)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [DeepMDP: Learning Continuous Latent Space Models for Representation Learning](https://mlsanity.com/paper/1906.02736/) (2019)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Towards Interpretable Reinforcement Learning Using Attention Augmented Agents](https://mlsanity.com/paper/1906.02500/) (2019)
- [On Improving Deep Reinforcement Learning for POMDPs](https://mlsanity.com/paper/1804.06309/) (2018)
- [Neuroevolution of Self-Interpretable Agents](https://mlsanity.com/paper/2003.08165/) (2020)
- [Online Planning Algorithms for POMDPs](https://mlsanity.com/paper/1401.3436/) (2014)
- [Deep Hierarchical Reinforcement Learning Algorithm in Partially Observable Markov Decision Processes](https://mlsanity.com/paper/1805.04419/) (2018)
- [Influence-aware Memory Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1911.07643/) (2019)

## Built on by (most cited first, 1 of 1)

- [An Approach to Partial Observability in Games: Learning to Both Act and Observe](https://mlsanity.com/paper/2108.05701/) (2021)

## Abstract (arXiv)

Faced with an ever-increasing complexity of their domains of application, artificial learning agents are now able to scale up in their ability to process an overwhelming amount of information coming from their interaction with an environment. However, this process of scaling does come with a cost of encoding and processing an increasing amount of redundant information that is not necessarily beneficial to the learning process itself. This work exploits the properties of the learning systems defined over partially observable domains by selectively focusing on the specific type of information that is more likely to express the causal interaction among the transitioning states of the environment. Adaptive masking of the observation space based on the temporal difference displacement criterion enabled a significant improvement in convergence of temporal difference algorithms defined over a partially observable Markov process.