Push Q-Learning agent to Hub

.gitattributes

@@ -29,3 +29,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,35 @@
+---
+tags:
+- Taxi-v3
+- q-learning
+- reinforcement-learning
+- custom-implementation
+model-index:
+- name: q-Taxi-v3
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 7.48 +/- 2.72
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Taxi-v3
+      type: Taxi-v3
+---
+
+  # **Q-Learning** Agent playing **Taxi-v3**
+  This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
+  
+  ## Usage
+  ```python
+  model = load_from_hub(repo_id="jasheershihab/q-Taxi-v3", filename="q-learning.pkl")
+
+  # Don't forget to check if you need to add additional attributes (is_slippery=False etc)
+  env = gym.make(model["env_id"])
+
+  evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
+  
+  ```
+  

q-learning.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:efa1bd3e5bbc08d0d2f2275a1b321bd7715e0ad3d9fa3828c0b890068b3cc2b2
+size 24589

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:39899d7c43b6fee68e66fa6c5e12ca12ffb89107034d81e47a3ebf87b7345faf
+size 120601

results.json

@@ -0,0 +1 @@
+{"env_id": "Taxi-v3", "mean_reward": 7.48, "n_eval_episodes": 100, "eval_datetime": "2022-08-16T06:32:02.894525"}