This is the Windows app named All RL Algorithms from Scratch whose latest release can be downloaded as all-rl-algorithmssourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
All RL Algorithms from Scratch
DESCRIPTION:
All RL Algorithms from Scratch is an educational reinforcement learning repository built around readable Python and Jupyter Notebook implementations. Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. Implemented topics include Q-learning, SARSA, Expected SARSA, Dyna-Q, REINFORCE, PPO, A2C, A3C, DDPG, SAC, TRPO, DQN, MADDPG, QMIX, HAC, MCTS, and PlaNet. The code prioritizes clarity, experimentation, and mathematical intuition over production speed. A companion cheat sheet gives learners a quick reference for formulas, pseudocode, and key concepts.
Features
- 18 reinforcement learning notebooks
- Beginner-friendly Python implementations
- Value-based and policy-based algorithms
- Actor-critic and model-based coverage
- Multi-agent reinforcement learning examples
- Comprehensive RL cheat sheet
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/all-rl-algorithms.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.