Update README.md

README.md

@@ -1,27 +1,35 @@
----
-library_name: stable-baselines3
-tags:
-- SpaceInvadersNoFrameskip-v4
-- deep-reinforcement-learning
-- reinforcement-learning
-- stable-baselines3
-model-index:
-- name: DQN
-  results:
-  - task:
-      type: reinforcement-learning
-      name: reinforcement-learning
-    dataset:
-      name: SpaceInvadersNoFrameskip-v4
-      type: SpaceInvadersNoFrameskip-v4
-    metrics:
-    - type: mean_reward
-      value: 148.00 +/- 49.31
-      name: mean_reward
-      verified: false
----
+---
+library_name: stable-baselines3
+tags:
+- SpaceInvadersNoFrameskip-v4
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: DQN
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: SpaceInvadersNoFrameskip-v4
+      type: SpaceInvadersNoFrameskip-v4
+    metrics:
+    - type: mean_reward
+      value: 148.00 +/- 49.31
+      name: mean_reward
+      verified: false
+license: wtfpl
+---
 
+# **8/15-16/24 : I am curently trying to improve the DQN algorithm**
+# **to reach a score above 200 within a limited processing time**
+# **(1 hr GPU, 3 hrs CPU, max 500000 timesteps).**
+# **I am open to hyperparameters/referrals/suggestions! Thanks :)**
+
+<br><br><br><br><br><br><br><br><br><br>
 # **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
+
 This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
 and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
@@ -83,4 +91,4 @@ OrderedDict([('batch_size', 32),
 # Environment Arguments
 ```python
 {'render_mode': 'rgb_array'}
-```
+```