kirubel1738/lunarlander-ddqn

Double Deep Q-Network (Double DQN) — LunarLander-v3

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

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Environment

• Environment: LunarLander-v3
• State Space: 8-dimensional continuous vector
• Action Space: 4 discrete thruster actions
• Goal: Land spacecraft safely between flags

LunarLander is significantly more complex than CartPole:

• Higher dimensional state
• Complex reward shaping
• High variance returns

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Why Standard DQN Is Not Enough

Standard DQN uses:

y = r + γ max_a' Q(s',a'; θ⁻)

The max operator introduces overestimation bias:

E[max(X)] ≥ max(E[X])

This bias becomes significant in complex environments.

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Double DQN Solution

Double DQN separates:

1. Action selection (policy network)
2. Action evaluation (target network)

Target becomes:

y = r + γ Q(s', argmax_a Q(s',a; θ), θ⁻)

This reduces overestimation and improves stability.

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Training Details

• Learning rate: 1e-4
• Discount factor: 0.99
• Batch size: 64
• Replay buffer: 50,000
• Target update frequency: 500 steps
• Episodes: 2000

Lower learning rate improves stability in complex environments.

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Performance

The agent learns to perform controlled landings and achieve high average rewards.

Double DQN provides more stable convergence compared to standard DQN.

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Visualization

Below is the trained Double DQN agent landing:

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Files

• lunarlander_ddqn.pt → Trained PyTorch model
• lunarlander_ddqn.gif → Agent demonstration

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Summary

This project demonstrates:

• Overestimation bias in DQN
• Double DQN improvement
• Stability scaling in deep RL
• Advanced reinforcement learning implementation

It represents a progression toward modern deep reinforcement learning techniques.