spragunr/deep_q_rl

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1094 stars · 341 forks observed · 2026-08-28

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

A Theano/Lasagne-based implementation of the Deep Q-Network (DQN) algorithm from DeepMind's Atari reinforcement learning papers. It trains neural network agents to play Atari games via the Arcade Learning Environment.

Use cases

• reproduce the DQN Atari results from the NIPS 2013 and Nature 2015 papers
• train a deep Q-learning agent to play Atari games
• study an educational reference implementation of deep reinforcement learning
• experiment with deep Q-learning hyperparameters on classic RL benchmarks

When to choose

• you want to reproduce or study the original DQN papers' results
• you need a simple, readable reference implementation of deep Q-learning
• you are doing research or coursework on deep reinforcement learning with legacy Theano tooling

When to avoid

• you need a maintained framework for modern RL research (use stable-baselines3, RLlib, or CleanRL)
• your project runs on modern PyTorch/TensorFlow stacks
• you need Windows or CPU-only training support
• you cannot invest days of GPU training time

Facets

library · maturity abandoned

reinforcement-learning deep-learning machine-learning reinforcement-learning machine-learning deep-learning gaming-tools python deep-q-learning theano lasagne atari dqn arcade-learning-environment linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="spragunr/deep_q_rl")