Fix broken links for continuous control examples.
@@ -26,14 +26,11 @@ Suite, will require a [MuJoCo license](https://www.roboti.us/license.html) in
order to run. See our [tutorial] for more details or see refer to the
[dm_control] repository for further information.
-- [D4PG](control/run_d4pg.py): a deterministic policy gradient (D4PG) agent
- which includes a determinstic policy and a distributional critic running on
- the DeepMind Control Suite.
-- [D4PG (gym)](control/run_d4pg_gym.py): this example runs the same algorithm
- on a number of tasks defined in the [OpenAI Gym]. By default this will run
- the "mountain car" domain which does not require a MuJoCo license.
-- [DMPO](control/run_dmpo.py): a maximum-a-posterior policy optimization
- agent which combines both a distributional critic and a stochastic policy.
+- [D4PG](https://github.com/deepmind/acme/blob/master/examples/baselines/rl_continuous/run_d4pg.py): a deterministic policy gradient (D4PG) agent which includes a determinstic
+ policy and a distributional critic running on the DeepMind Control Suite or
+ the [OpenAI Gym]. By default it runs on the "half cheetah" environment from the
+ OpenAI Gym.
+- [MPO](https://github.com/deepmind/acme/blob/master/examples/baselines/rl_continuous/run_mpo.py): a maximum-a-posterior policy optimization agent which combines both a distributional critic and a stochastic policy.
[dm_control]: https://github.com/deepmind/dm_control
[OpenAI Gym]: https://github.com/openai/gym
