From 6b827d221e0ac10f60800e288d0a348f7f96247e Mon Sep 17 00:00:00 2001
From: Misha Brukman <mbrukman@google.com>
Date: Tue, 13 Dec 2022 13:02:09 -0800
Subject: [PATCH] Add link to Rich Sutton's website for the quote and use quote
 formatting to clarify visually that it's a quote. Remove italics of the whole
 text so that it can be used for the words "search" and "learning" that are
 emphasized in the original.

Use relative paths to code in the same repo. Also, minor formatting & grammar fixes.

PiperOrigin-RevId: 495103568
---
 README.md | 29 +++++++++++++++--------------
 1 file changed, 15 insertions(+), 14 deletions(-)

diff --git a/README.md b/README.md
index 6fa9141..4addc54 100644
--- a/README.md
+++ b/README.md
@@ -27,11 +27,12 @@ pip install git+https://github.com/deepmind/mctx.git
 ## Motivation
 
 Learning and search have been important topics since the early days of AI
-research. In the words of Rich Sutton: *One thing that should be learned [...]
-is the great power of general purpose methods, of methods that continue to scale
-with increased computation even as the available computation becomes very great.
-The two methods that seem to scale arbitrarily in this way are search and
-learning.*
+research. In the [words of Rich Sutton](http://www.incompleteideas.net/IncIdeas/BitterLesson.html):
+
+> One thing that should be learned [...] is the great power of general purpose
+> methods, of methods that continue to scale with increased computation even as
+> the available computation becomes very great. The two methods that seem to
+> scale arbitrarily in this way are *search* and *learning*.
 
 Recently, search algorithms have been successfully combined with learned models
 parameterized by deep neural networks, resulting in some of the most powerful
@@ -39,7 +40,7 @@ and general reinforcement learning algorithms to date (e.g. MuZero).
 However, using search algorithms in combination with deep neural networks
 requires efficient implementations, typically written in fast compiled
 languages; this can come at the expense of usability and hackability,
-especially for researchers that are not familiar with C++. In turn this limits
+especially for researchers that are not familiar with C++. In turn, this limits
 adoption and further research on this critical topic.
 
 Through this library, we hope to help researchers everywhere to contribute to
@@ -92,8 +93,8 @@ new_embedding)` with a `RecurrentFnOutput` and the embedding of the next state.
 The `RecurrentFnOutput` contains the `reward` and `discount` for the transition,
 and `prior_logits` and `value` for the new state.
 
-In [examples/visualization_demo.py](https://github.com/deepmind/mctx/blob/main/examples/visualization_demo.py)
-you can see calls to a policy:
+In [`examples/visualization_demo.py`](https://github.com/deepmind/mctx/blob/main/examples/visualization_demo.py), you can
+see calls to a policy:
 
 ```python
 policy_output = mctx.gumbel_muzero_policy(params, rng_key, root, recurrent_fn,
@@ -109,22 +110,22 @@ We recommend to use the `gumbel_muzero_policy`.
 [Gumbel MuZero](https://openreview.net/forum?id=bERaNdoegnO) guarantees a policy
 improvement if the action values are correctly evaluated. The policy improvement
 is demonstrated in
-[examples/policy_improvement_demo.py](https://github.com/deepmind/mctx/blob/main/examples/policy_improvement_demo.py).
+[`examples/policy_improvement_demo.py`](https://github.com/deepmind/mctx/blob/main/examples/policy_improvement_demo.py).
 
 ### Example projects
 The following projects demonstrate the Mctx usage:
 
-- [Basic Learning Demo with Mctx](https://github.com/kenjyoung/mctx_learning_demo)
-... AlphaZero on random mazes.
-- [a0-jax](https://github.com/NTT123/a0-jax) ... AlphaZero on Connect Four,
-Gomoku, and Go.
+- [Basic Learning Demo with Mctx](https://github.com/kenjyoung/mctx_learning_demo) —
+  AlphaZero on random mazes.
+- [a0-jax](https://github.com/NTT123/a0-jax) — AlphaZero on Connect Four,
+  Gomoku, and Go.
 
 Tell us about your project.
 
 ## Citing Mctx
 
 This is not an officially supported Google product. Mctx is part of the
-[DeepMind JAX Ecosystem], to cite Mctx please use the [DeepMind JAX Ecosystem
+[DeepMind JAX Ecosystem]; to cite Mctx, please use the [DeepMind JAX Ecosystem
 citation].
 
 [DeepMind JAX Ecosystem]: https://deepmind.com/blog/article/using-jax-to-accelerate-our-research "DeepMind JAX Ecosystem"