Fix more docs.
@@ -11,8 +11,8 @@ in-depth look at the construction of the D4PG agent. This also highlights the
general structure of most Acme agents which applies more broadly to all agents
implemented in Acme.
-[quickstart]: /../../blob/master/acme/examples/quickstart.ipynb
-[tutorial]: /../../blob/master/acme/examples/tutorial.ipynb
+[quickstart]: /../../blob/master/examples/quickstart.ipynb
+[tutorial]: /../../blob/master/examples/tutorial.ipynb
## Continuous control
@@ -47,8 +47,8 @@ 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/acme/examples/atari/run_dqn.py): a "classic" benchmark agent for Atari; and
-- [R2D2](/../../blob/master/acme/examples/atari/run_r2d2.py): a more recent Q-learning variant which includes
+- [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
@@ -59,15 +59,15 @@ running on this environment.
Acme includes examples of offline agents, i.e. agents trained using external
data generated by another agent:
-- [BC](/../../blob/master/acme/examples/offline/run_bc.py): a behaviour cloning agent.
-- [BC (JAX)](/../../blob/master/acme/examples/offline/run_bc_jax.py): a behaviour cloning agent (implemented
+- [BC](/../../blob/master/examples/offline/run_bc.py): a behaviour cloning agent.
+- [BC (JAX)](/../../blob/master/examples/offline/run_bc_jax.py): a behaviour cloning agent (implemented
in jax).
-- [BCQ](/../../blob/master/acme/examples/offline/run_bcq.py): an implementation of BCQ.
+- [BCQ](/../../blob/master/examples/offline/run_bcq.py): an implementation of BCQ.
Similarly we also include so-called "from demonstration" agents which mix
offline and online data:
-- [DQfD](/../../blob/master/acme/examples/offline/run_dqfd.py): the DQfD agent running on hard-exploration
+- [DQfD](/../../blob/master/examples/offline/run_dqfd.py): the DQfD agent running on hard-exploration
tasks within bsuite (e.g. deep sea) using demonstration data; and
@@ -78,9 +78,9 @@ of tasks and environments which collectively investigate core capabilities of RL
algorithms across a number of different axes. The examples we include
show how to run Acme agents on this suite.
-- [DQN](/../../blob/master/acme/examples/bsuite/run_dqn.py): an off-policy DQN examples;
-- [Impala](/../../blob/master/acme/examples/bsuite/run_impala.py): an on-policy Impala agent; and
-- [MCTS](/../../blob/master/acme/examples/bsuite/run_mcts.py): a model-based agent running on the
+- [DQN](/../../blob/master/examples/bsuite/run_dqn.py): an off-policy DQN examples;
+- [Impala](/../../blob/master/examples/bsuite/run_impala.py): an on-policy Impala agent; and
+- [MCTS](/../../blob/master/examples/bsuite/run_mcts.py): a model-based agent running on the
task suite using either a simulator of the environment or a learned model.
For more information see https://github.com/deepmind/bsuite.
