---
title: "Asynchronous Methods for Deep Reinforcement Learning"
description: "A3C replaced one agent's replay memory with many fresh, out-of-phase actor-learners updating a shared model. Follow how parallel experience decorrelates training data, how short n-step rollouts update both policy and value, and why later synchronous A2C changed the verdict on asynchrony itself."
canonical_url: "https://fanout.sh/daily/2026-10-30-asynchronous-deep-reinforcement-learning"
md_url: "https://fanout.sh/daily/2026-10-30-asynchronous-deep-reinforcement-learning.md"
last_updated: "2026-10-30"
access: "public"
---

# Asynchronous Methods for Deep Reinforcement Learning

A3C replaced one agent's replay memory with many fresh, out-of-phase actor-learners updating a shared model. Follow how parallel experience decorrelates training data, how short n-step rollouts update both policy and value, and why later synchronous A2C changed the verdict on asynchrony itself.

## Paper details

- Authors: Volodymyr Mnih et al.

- Venue: ICML 2016

- Track: AI Research

- Difficulty: Hard

- Reading time: 13 min

- Original paper: https://arxiv.org/abs/1602.01783v2

## What you will learn

- Why independent actors can perform the stabilizing, decorrelating role that experience replay played in DQN.

- How an A3C worker turns a short rollout into an advantage estimate and updates shared policy and value parameters without a synchronization barrier.

- What the 57-game Atari and thread-scaling results establish, which comparison caveat matters, and why A2C later separated parallelism from asynchrony.

## Continue exploring

[Compare replay-based DQN](https://fanout.sh/daily/2026-09-28-human-level-control-dqn): Contrast A3C's fresh parallel rollouts with DQN's stored, randomly sampled experience and delayed target network.

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