Create README.md


README.md

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+---
+license: mit
+language: en
+datasets:
+  - gymnasium
+metrics:
+  - episode_reward
+pipeline_tag: reinforcement-learning
+tags:
+  - reinforcement-learning
+  - q-learning
+  - tabular-rl
+  - gymnasium
+  - frozenlake
+library_name: numpy
+---
+
+# Q-Learning Agent — FrozenLake-v1
+
+This repository contains a trained **Q-Learning agent** for the Gymnasium environment **FrozenLake-v1**.
+
+---
+
+## Environment
+
+- **Environment:** FrozenLake-v1  
+- **State Space:** 16 discrete states  
+- **Action Space:** 4 discrete actions  
+- **Type:** Stochastic grid-world (slippery surface)
+
+FrozenLake is a small Markov Decision Process (MDP) where the agent must reach a goal while avoiding holes.
+
+---
+
+## Algorithm
+
+This model uses **Tabular Q-Learning**, a model-free off-policy reinforcement learning algorithm.
+
+Update rule:
+
+Q(s,a) ← Q(s,a) + α [ r + γ max_a' Q(s',a') − Q(s,a) ]
+
+Where:
+- α = learning rate
+- γ = discount factor
+
+Because the environment is discrete and small, Q-values are stored in a Q-table of shape (16 × 4).
+
+---
+
+## Training Details
+
+- Learning rate (α): 0.1  
+- Discount factor (γ): 0.99  
+- Episodes: 5000  
+- Epsilon-greedy exploration with decay  
+
+The agent learns to maximize expected long-term reward despite stochastic transitions.
+
+---
+
+## Performance
+
+Training reward was tracked across episodes.
+
+The agent successfully learns an optimal navigation policy to reach the goal.
+
+---
+
+## Visualization
+
+Below is the trained agent interacting with the environment:
+
+![FrozenLake Agent](./frozenlake_trained_agent.gif)
+
+---
+
+## Files
+
+- `frozenlake_q_table.npy` → Trained Q-table  
+- `frozenlake_trained_agent.gif` → Agent demonstration  
+
+---
+
+## Summary
+
+This project demonstrates:
+
+- Tabular reinforcement learning
+- Bellman optimality updates
+- Exploration vs exploitation trade-off
+- Convergence in finite MDPs
+
+It serves as a foundational reinforcement learning example.