seungeunrho/minimalRL resource

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3222 stars · 491 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A collection of minimal PyTorch implementations of classic deep reinforcement learning algorithms (REINFORCE, DQN, PPO, DDPG, A3C, A2C, ACER, SAC, and more), each fitting in a single 100-150 line file. It is designed for learning, with every algorithm trainable on CartPole-v1 in under 30 seconds without a GPU.

Use cases

• learn reinforcement learning algorithms from short readable code
• understand how DQN or PPO works by reading a single file
• quickly train basic RL agents on CartPole without a GPU
• study PyTorch implementations of policy gradient methods
• get a minimal reference implementation of SAC or DDPG
• teach a deep RL course with simple code examples

When to choose

• you want to learn or teach RL algorithm internals with minimal code
• you need compact reference implementations to read or modify
• you want fast, CPU-friendly experiments on simple environments

When to avoid

• you need production-ready, scalable, or feature-rich RL training
• you need complex environments beyond CartPole-v1
• you need a maintained framework with active development and support

Facets

learning-resource · maturity maintenance

reinforcement-learning machine-learning deep-learning reinforcement-learning machine-learning deep-learning education tutorials python cross-platform pytorch reinforcement-learning-algorithms educational minimal-implementations cartpole openai-gym policy-gradients dqn ppo sac a3c

Member repositories

For agents

markdown · JSON · MCP: product_card(name="seungeunrho/minimalRL")