all rl algorithms from scratch

We have hosted the application all rl algorithms from scratch in order to run this application in our online workstations with Wine or directly.

Quick description about all rl algorithms from scratch:

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.
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