More doc fixing.
@@ -4,7 +4,7 @@ This directory includes a number of working examples of Acme agents. These
examples are not meant to be comprehensive and instead show a number of common
use cases under which Acme agents can be applied.
-Our [quickstart guide](quickstart) can be used to get running quickly. This
+Our [quickstart] guide can be used to get running quickly. This
notebook will show how to instantiate a simple agent and run it on an
environment. You can also take a look at our [tutorial], which takes a more
in-depth look at the construction of the D4PG agent. This also highlights the
@@ -41,17 +41,15 @@ order to run. See our [tutorial] for more details or see refer to the
## Discrete agents (Atari)
-The development of the [Arcade Learning environment](ale) and the coinciding use
+The development of the [Arcade Learning environment] and the coinciding use
of Atari as a benchmark has played a very prominent role in the modern usage and
testing of reinforcement learning algorithms. As a result we've also included
direct examples of prominent discrete-action algorithms implemented in Acme and
running on this environment.
- [DQN](/../../blob/master/examples/atari/run_dqn.py): a "classic" benchmark agent for Atari; and
-- [R2D2](/../../blob/master/examples/atari/run_r2d2.py): a more recent Q-learning variant which includes
- recurrence.
-[ale]: https://arxiv.org/abs/1207.4708
+[Arcade Learning environment]: https://arxiv.org/abs/1207.4708
## Offline agents
