---
title: "Deep reinforcement learning agents · Artificial systems · Entities · Simulation Hypothesis"
url: https://simulation-hypothesis.com/entities/ai-drl
description: "Neural networks trained by trial and error to maximise a reward signal."
license: https://creativecommons.org/licenses/by/4.0/
credit: "Ross Allan Dahl, Simulation Hypothesis (simulation-hypothesis.com)"
---

[Entities](https://simulation-hypothesis.com/entities) › Artificial systems

# Deep reinforcement learning agents

Neural networks trained by trial and error to maximise a reward signal. They learn a policy or value function by interacting with an environment, often a simulated one such as a game.

Neural networks trained by trial and error to maximise a reward signal. They learn a policy or value function by interacting with an environment, often a simulated one such as a game. They can reach superhuman skill at narrow tasks, but they're optimised for a single reward goal.

## Sources

* Mnih V, Kavukcuoglu K, Silver D, Rusu AA, et al. (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529-533\. doi:10.1038/nature14236.
* Silver D et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature 529:484-489\. doi:10.1038/nature16961.

Assess it against the published criteria