---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- dqn
- dueling-dqn
- double-dqn
- lunar-lander
- gymnasium
- pytorch
model-index:
- name: jongchullee/lunar-lander-dueling-dqn
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: LunarLander-v3
      type: LunarLander-v3
    metrics:
    - type: mean_reward
      value: 289.2
      name: Mean Reward
---

# LunarLander-v3 Dueling Double-DQN Agent

This is a trained **Dueling Double-DQN** agent capable of performing perfect, smooth, and robust landings in the **Gymnasium LunarLander-v3** environment with an evaluation score of **289.2**.

## Algorithm & Training Details
- **Algorithm**: Dueling Double-DQN (D3QN)
- **Framework**: PyTorch + Gymnasium
- **State Space**: 8 dimensions (Position, Velocity, Angle, Angular Velocity, Leg contacts)
- **Action Space**: Discrete(4) - [0: Do Nothing, 1: Fire Left RCS, 2: Fire Main Engine, 3: Fire Right RCS]
- **Exploration (Epsilon)**: $1.0 (100\%) \rightarrow 0.05 (5\%)$
- **Loss Function**: Smooth L1 (Huber Loss)
- **Target Network Update**: Soft Polyak Update ($\tau = 0.005$)
- **Peak Score**: **+289.2**

## How to Run Inference

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

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

# 2. Load Agent
agent = DQNAgent(state_dim=8, action_dim=4)
agent.load("best_lunar_lander_dqn.pth")

# 3. Simulate
total_reward = 0
done = False
while not done:
    action, _ = 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"Final Landing Reward: {total_reward:.2f}")
env.close()
```