DQN-Atari

Deep Q-network implementation for Pong-vo. The implementation follows from the paper - Playing Atari with Deep Reinforcement Learning and Human-level control through deep reinforcement learning.

Results

Video of Gameplay - DQN Nature Paper

Reward per Episode

Summary of Implementation

DQN Nature Architecture Implementation

• Input : 84 × 84 × 4 image (using the last 4 frames of a history)
• Conv Layer 1 : 32 8 × 8 filters with stride 4
• Conv Layer 2: 64 4 × 4 filters with stride 2
• Conv Layer 3: 64 3 × 3 filters with stride 1
• Fully Connected 1 : fully-connected and consists of 256 rectifier units
• Output : fully connected linear layer with a single output for each valid action.

DQN Neurips Architecture Implementation

• Input : 84 × 84 × 4 image (using the last 4 frames of a history)
• Conv Layer 1 : 16 8 × 8 filters with stride 4
• Conv Layer 2: 32 4 × 4 filters with stride 2
• Fully Connected 1 : fully-connected and consists of 256 rectifier units
• Output : fully connected linear layer with a single output for each valid action.

Other Params

• Optimizer: RMSProp
• Batch Size: 32
• E-greedy : 0.1

How to run

Create a new environment

Example:

conda create -n dqn_pong

Install Dependencies

pip install -r requirements.txt

To use gym.wrappers.Monitor to record the last episode

sudo apt-get install ffmpeg

Run Training from Scratch

python train_atari.py

Use a trained agent

python train_atari.py --load-checkpoint-file results/checkpoint_dqn_nature.pth

View Progress

A video is recorded every 50 episodes. See videos in /video/ folder.