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docs: add logo (#3)
* docs: readme updates * chore: update logo * docs: nicer logo
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README.md

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# echo
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<p align="center">
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<img src="docs/src/assets/logo.svg" alt="echo" width="320">
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</p>
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<p align="center">
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<a href="https://github.com/instadeepai/echo/actions/workflows/ci.yml"><img src="https://github.com/instadeepai/echo/actions/workflows/ci.yml/badge.svg" alt="CI"></a>
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<a href="https://instadeepai.github.io/echo/"><img src="https://img.shields.io/badge/docs-instadeepai.github.io%2Fecho-blue?style=flat-square&logo=materialformkdocs" alt="Docs"></a>
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<a href="https://pypi.org/project/id-echo/"><img src="https://img.shields.io/pypi/v/id-echo?style=flat-square&logo=pypi&logoColor=white" alt="PyPI"></a>
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<a href="https://github.com/instadeepai/echo/blob/main/pyproject.toml"><img src="https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Finstadeepai%2Fecho%2Fmain%2Fpyproject.toml&style=flat-square&logo=python&logoColor=white&label=python" alt="Python"></a>
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as zero-copy numpy views, with the GIL released while you wait for the next
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batch.
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<p align="center"><a href="https://instadeepai.github.io/echo/"><b>📖 Documentation</b></a></p>
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## Install
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```bash
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## Example
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A typical setup runs the server on the learner node and clients on rollout
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workers (separate processes or machines).
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workers (separate processes or machines). See more detailed examples in the [documentation](https://instadeepai.github.io/echo/).
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**Learner side** (one process):
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docs/src/assets/logo.svg

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docs/src/index.md

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# echo
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<p align="center">
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<img src="assets/logo.svg" alt="echo" width="320">
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</p>
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A very fast distributed replay buffer for reinforcement learning. The core
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is a lockfree, pre-allocated Rust ring buffer; batches come back to Python

mkdocs.yml

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theme:
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name: material
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logo: assets/logo.svg
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features:
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- navigation.tabs
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- navigation.sections

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