nikitasrivatsan/DeepLearningVideoGames resource

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1090 stars · 216 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A research project implementing Deep Q Networks (DQN) to learn video game strategies for Pong and Tetris from raw pixel input. It uses a convolutional neural network trained with Q learning to approximate expected rewards and select optimal actions.

Use cases

• learn to play pong with deep reinforcement learning
• train a dqn on atari games from pixels
• example of q learning with convolutional neural networks
• study deep q network implementation in python
• reproduce deepmind atari results
• learn reinforcement learning for video games

When to choose

• learning how DQN works with a small readable codebase
• reproducing classic deep RL paper results on Pong or Tetris
• teaching or studying deep reinforcement learning from pixels

When to avoid

• production reinforcement learning systems
• modern high-performance RL training with maintained libraries
• projects requiring support or recent updates

Facets

learning-resource · maturity maintenance

deep-learning reinforcement-learning machine-learning image-processing reinforcement-learning deep-learning machine-learning gaming-tools python deep-q-networks q-learning atari pong tetris convolutional-neural-networks research-project

Member repositories

For agents

markdown · JSON · MCP: product_card(name="nikitasrivatsan/DeepLearningVideoGames")