---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- dqn
- dueling-dqn
- double-dqn
- lunar-lander
- gymnasium
- pytorch
library_name: gymnasium
---

# 🛰️ Double Dueling DQN Agent - LunarLander-v3 (Stylish Precision Landing)

This repository contains a trained **Double Dueling Deep Q-Network (DQN)** agent for the **Gymnasium `LunarLander-v3`** environment.

The agent was trained for **1000 episodes** with an **Epsilon exploration schedule decaying from 100% (1.0) to 5% (0.05)**, combined with **custom stylish & smooth landing reward shaping** (vertical attitude control $\theta \approx 0$, soft touchdown descent rate, and helipad center alignment).

---

## 🎯 Model Hyperparameters

| Parameter | Value |
| :--- | :--- |
| **Algorithm** | Double Dueling DQN |
| **Environment** | Gymnasium `LunarLander-v3` |
| **State Dimension** | 8 |
| **Action Dimension** | 4 (Discrete: Idle, Left RCS, Main Thruster, Right RCS) |
| **Total Episodes** | 1000 |
| **Epsilon Schedule** | 1.0 (100%) $\rightarrow$ 0.05 (5%) |
| **Discount Factor ($\gamma$)** | 0.99 |
| **Target Update** | Soft Polyak Averaging ($\tau = 0.005$) |
| **Learning Rate** | 5e-4 (Adam Optimizer) |
| **Loss Function** | Smooth L1 (Huber Loss) |
| **Replay Buffer Capacity** | 100,000 |
| **Batch Size** | 64 |

---

## 🚀 How to Load and Test the Model

```python
import torch
import gymnasium as gym
from lunar_dqn_agent import DQNAgent

# 1. Initialize environment
env = gym.make("LunarLander-v3", render_mode="human")
state, _ = env.reset(seed=42)

# 2. Load trained DQN Agent
agent = DQNAgent()
agent.load("best_lunar_dqn.pth")

total_reward = 0
done = False

while not done:
    # Select best greedy action (evaluation mode)
    action, q_values, _ = agent.select_action(state, evaluate=True)
    next_state, reward, terminated, truncated, _ = env.step(action)
    done = terminated or truncated
    state = next_state
    total_reward += reward

print(f"Landing Completed! Total Reward: {total_reward:.2f}")
env.close()
```

---

## 🏆 Features
- **Smooth Descent**: Soft vertical velocity control preventing hard impacts.
- **Upright Posture**: High angular stability with minimum wobbling.
- **Precision Touchdown**: Guided landing directly between the target flags.