Remove link for JAX D4PG (coming soon!).
@@ -14,7 +14,7 @@ setting:
Agent | Paper | Code
-------------------------------------------------------------- | :----------------------: | :--:
Deep Deterministic Policy Gradient (DDPG) | Lillicrap et al., 2015 | [![TF](../../docs/logos/tf-small.png)][DDPG_TF2]
-Distributed Distributional Deep Determinist (D4PG) | Barth-Maron et al., 2018 | [![TF](../../docs/logos/tf-small.png)][D4PG_TF2] [![JAX](../../docs/logos/jax-small.png)][D4PG_JAX]
+Distributed Distributional Deep Determinist (D4PG) | Barth-Maron et al., 2018 | [![TF](../../docs/logos/tf-small.png)][D4PG_TF2] ![JAX](../../docs/logos/jax-small.png) (coming soon!)
Maximum a posteriori Policy Optimisation (MPO) | Abdolmaleki et al., 2018 | [![TF](../../docs/logos/tf-small.png)][MPO_TF2]
Distributional Maximum a posteriori Policy Optimisation (DMPO) | - | [![TF](../../docs/logos/tf-small.png)][DMPO_TF2]
@@ -38,7 +38,7 @@ for use in Batch RL (with no environment interactions). Implemented algorithms
include:
Agent | Paper | Code
------ | :---: | :------------------------------:
+--------------------- | :---: | :---------------------------------:
Behavior Cloning (BC) | - | [![TF](../../docs/logos/tf-small.png)][BC_TF2]
### Learning from demonstrations
@@ -47,7 +47,7 @@ Acme also easily allows active data acquisition to be combined with data from
demonstrations. Such algorithms include:
Agent | Paper | Code
------ | :-------------------: | :--------------------------------:
+----------------------------------------------------------- | :-------------------: | :--:
Deep Q-Learning from Demonstrations (DQfD) | Hester et al., 2017 | [![TF](../../docs/logos/tf-small.png)][DQFD_TF2]
Recurrent Replay Distributed DQN from Demonstratinos (R2D3) | Gulcehre et al., 2020 | [![TF](../../docs/logos/tf-small.png)][R2D3_TF2]
