From bc47d3dcf35accac196fb7f469cb9cb67cc03f11 Mon Sep 17 00:00:00 2001
From: Callum Tilbury <37700709+callumtilbury@users.noreply.github.com>
Date: Tue, 6 Feb 2024 15:20:25 +0200
Subject: [PATCH] fix: mkdocs render issue of asterisk

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 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index e69566a..9427afb 100644
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+++ b/README.md
@@ -149,7 +149,7 @@ from CleanRLs DQN JAX example.
 like Snake for our fully jitted examples.
 
 ## Vault 💾
-Vault is an efficient mechanism for saving Flashbax buffers to persistent data storage, e.g. for use in offline reinforcement learning. Consider a Flashbax buffer which has experience data of dimensionality $(B, T, *E)$, where $B$ is a batch dimension (for the sake of recording independent trajectories synchronously), $T$ is a temporal/sequential dimension, and $*E$ indicates the one or more dimensions of the experience data itself. Since large quantities of data may be generated for a given environment, Vault extends the $T$ dimension to a virtually unconstrained degree by reading and writing slices of buffers along this temporal axis. In doing so, gigantic buffer stores can reside on disk, from which sub-buffers can be loaded into RAM/VRAM for efficient offline training. Vault has been tested with the item, flat, and trajectory buffers.
+Vault is an efficient mechanism for saving Flashbax buffers to persistent data storage, e.g. for use in offline reinforcement learning. Consider a Flashbax buffer which has experience data of dimensionality $(B, T, \*E)$, where $B$ is a batch dimension (for the sake of recording independent trajectories synchronously), $T$ is a temporal/sequential dimension, and $\*E$ indicates the one or more dimensions of the experience data itself. Since large quantities of data may be generated for a given environment, Vault extends the $T$ dimension to a virtually unconstrained degree by reading and writing slices of buffers along this temporal axis. In doing so, gigantic buffer stores can reside on disk, from which sub-buffers can be loaded into RAM/VRAM for efficient offline training. Vault has been tested with the item, flat, and trajectory buffers.
 
 For more information, see the demonstrative notebook: [![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/instadeepai/flashbax/blob/main/examples/vault_demonstration.ipynb)