# The Partially Observable History Process

arXiv: https://arxiv.org/abs/2111.08102 · HTML: https://mlsanity.com/paper/2111.08102/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 15 Nov 2021.

## Builds on (most cited first, 5 of 5)

- [Rethinking Formal Models of Partially Observable Multiagent Decision Making](https://mlsanity.com/paper/1906.11110/) (2019)
- [Actor-Critic Policy Optimization in Partially Observable Multiagent Environments](https://mlsanity.com/paper/1810.09026/) (2018)
- [Hindsight and Sequential Rationality of Correlated Play](https://mlsanity.com/paper/2012.05874/) (2020)
- [Online Convex Optimization for Sequential Decision Processes and Extensive-Form Games](https://mlsanity.com/paper/1809.03075/) (2018)
- [Efficient Deviation Types and Learning for Hindsight Rationality in Extensive-Form Games](https://mlsanity.com/paper/2102.06973/) (2021)

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

- [Rethinking the Foundations for Continual Reinforcement Learning](https://mlsanity.com/paper/2504.08161/) (2025)
- [Interpolating Between Softmax Policy Gradient and Neural Replicator Dynamics with Capped Implicit Exploration](https://mlsanity.com/paper/2206.02036/) (2022)

## Abstract (arXiv)

We introduce the partially observable history process (POHP) formalism for reinforcement learning. POHP centers around the actions and observations of a single agent and abstracts away the presence of other players without reducing them to stochastic processes. Our formalism provides a streamlined interface for designing algorithms that defy categorization as exclusively single or multi-agent, and for developing theory that applies across these domains. We show how the POHP formalism unifies traditional models including the Markov decision process, the Markov game, the extensive-form game, and their partially observable extensions, without introducing burdensome technical machinery or violating the philosophical underpinnings of reinforcement learning. We illustrate the utility of our formalism by concisely exploring observable sequential rationality, examining some theoretical properties of general immediate regret minimization, and generalizing the extensive-form regret minimization (EFR) algorithm.