This is the Windows app named minimalRL-pytorch whose latest release can be downloaded as minimalRLsourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
minimalRL-pytorch
DESCRIPTION:
minimalRL is a lightweight reinforcement learning repository that implements several classic algorithms using minimal PyTorch code. The project is designed primarily as an educational resource that demonstrates how reinforcement learning algorithms work internally without the complexity of large frameworks. Each algorithm implementation is contained within a single file and typically ranges from about 100 to 150 lines of code, making it easy for learners to inspect the entire implementation at once. The repository includes examples of widely used reinforcement learning methods such as REINFORCE, Deep Q-Networks, Proximal Policy Optimization, and Actor-Critic architectures. Most experiments are designed to run quickly using the CartPole environment so that users can focus on understanding algorithm logic rather than computational infrastructure.
Features
- Minimal PyTorch implementations of major reinforcement learning algorithms
- Single-file code implementations for clarity and educational purposes
- Support for algorithms such as DQN, PPO, REINFORCE, A3C, and SAC
- Fast training examples using the CartPole-v1 environment
- Code structure designed for quick experimentation and modification
- Simple dependency requirements including PyTorch and OpenAI Gym
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/minimalrl-pytorch.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.