Create README.md

README.md

@@ -0,0 +1,83 @@
+---
+library_name: numpy
+tags:
+- Taxi-v3
+- reinforcement-learning
+- q-learning
+- custom-implementation
+model-index:
+- name: Q-Learning
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: Taxi-v3
+      type: Taxi-v3
+    metrics:
+    - type: mean_reward
+      name: mean_reward
+      value: 7.92 +/- 2.60
+      verified: false
+---
+
+# 🚖 Q-Learning Agent for Taxi-v3
+
+This is a trained **Q-Learning agent** for the **Taxi-v3** environment using a **tabular approach**.
+
+## Developer
+**Vishand S (@Vishand03)**
+
+## Frameworks
+- Python  
+- NumPy  
+- Gymnasium
+
+## Training Details
+- Algorithm: Q-Learning  
+- Episodes: 2,000,000  
+- Max Steps per Episode: 200  
+- Learning rate (α): 0.1  
+- Discount factor (γ): 0.99  
+- Exploration: Epsilon-greedy  
+- Epsilon decay: 0.0005  
+- Mean Reward: ~7.92 ± 2.60  
+
+---
+
+## 🛠 Usage
+
+```python
+import gymnasium as gym
+import pickle
+from huggingface_hub import hf_hub_download
+
+# -------------------------
+# Load pretrained model
+# -------------------------
+model_file = hf_hub_download("Vishand03/q-Taxi-v3", "q-learning.pkl")
+with open(model_file, "rb") as f:
+    model = pickle.load(f)
+
+env = gym.make(model["env_id"])
+
+# -------------------------
+# Evaluate agent
+# -------------------------
+def greedy_policy(Qtable, state):
+    return max(range(len(Qtable[state])), key=lambda a: Qtable[state][a])
+
+total_rewards = []
+for _ in range(model["n_eval_episodes"]):
+    state, _ = env.reset()
+    done = False
+    episode_reward = 0
+    while not done:
+        action = greedy_policy(model["qtable"], state)
+        state, reward, terminated, truncated, _ = env.step(action)
+        episode_reward += reward
+        done = terminated or truncated
+    total_rewards.append(episode_reward)
+
+mean_reward = sum(total_rewards) / len(total_rewards)
+print(f"Mean Reward: {mean_reward:.2f}")